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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Multi-Relationship Recommendation Model Based on User Interest-Aware HU Xinrong, DENG Jiewen, LUO Ruiqi, LIU Junping, ZHU Qiang, PENG Tao Computer Engineering and Applications    2023, 59 (11): 231-240.   DOI: 10.3778/j.issn.1002-8331.2206-0148 Abstract （15）      PDF（pc） （782KB）（7）       Knowledge map    Save Because graph convolution network（GCN） can use the cooperation signals of high-order neighbors to learn the embedding of users and items better, it has been widely used in recommendation systems. However, in the current multi-relationship recommendation model based on GCN, the embedding learning of user nodes will be interfered by high-order adjacent users with dissimilar interests, so that users with different interests will get similar embedding after multi-layer graph convolution, resulting in the over-smoothing problem. Therefore, this paper proposes a multi-relationship recommendation model based on user interest-aware（IMRRM） because the above problem. First, the model uses a light graph convolutional network in the user-item heterogeneous interaction graph to obtain the graph structure information of each user. And then the subgraph generation module uses the user’s graph structure information and initial features to effectively identify users with similar interests, and group similar users and their interaction items into a subgraph. Finally, more accurate user embeddings are obtained by performing deep embedding learning in subgraphs to prevent unrelated high-order neighbors from propagating more negative information. Therefore, the IMRRM model reduces the influence of noise information on user embedding learning, effectively alleviates the over-smoothing problem, and makes multi-relationship recommendation more accurately. The effectiveness and robustness of IMRRM are verified by experiments on two public datasets, Beibei and Taobao. The experimental results show that the IMRRM model is improved by 1.98% and 1.49% on HR10, and 1.58% and 1.81% on NDCG10, respectively, with better performance. Reference | Related Articles | Metrics Select Article Topic-Based Heterogeneous Information Network for Venue Recommendation WANG Bingyuan, LIU Baisong, ZHANG Xueyuan, QIN Jiangcheng, DONG Qian, QIAN Jiangbo Computer Engineering and Applications    2023, 59 (11): 241-250.   DOI: 10.3778/j.issn.1002-8331.2207-0502 Abstract （13）      PDF（pc） （749KB）（2）       Knowledge map    Save With the expansion of academic information, scholars face a big challenge of efficiently selecting valid information in the era of big academic data. A venue recommendation system is one of the main ways to assist scholars in solving the information overload problem. This paper focuses on the issue of how to fit appropriate academic journals for manuscripts efficiently. It extracts diverse academic entities and edges from academic data for constructing academic heterogeneous information networks. This paper proposes a novel method for venue recommendation（SCVR）. Firstly, the topic information is extracted from the abstracts and topics by LDA and guides different types of nodes to map to the multi-topic feature space. Then, the meta-path contextual information is aggregated to the target node, forming a multi-topic node representation. Finally, the node representations from multiple mate-paths are combined into the final multi-topic node representations. SCVR learns the multi-topic node representations with paper content and network structure to venue recommendation.Experiments on two real academic datasets show that a heterogeneous information network recommendation incorporating article topics can effectively improve the performance of the venue recommendation. Compared with the current heterogeneous information network recommendation and traditional venue recommendation, the performance of SCVR has improved by an average of 2.7% and 19%, which indicates that SCVR has better performance in the area of venue recommendation. Reference | Related Articles | Metrics Select Anomaly Series Detection Algorithm Based on Segmentation Feature Representation SONG Chunlei, ZHAO Xujun, GAO Yaxing, JIN Guangyin Computer Engineering and Applications    2023, 59 (9): 262-271.   DOI: 10.3778/j.issn.1002-8331.2205-0271 Abstract （31）      PDF（pc） （820KB）（18）       Knowledge map    Save The supervised anomaly detection method of time series usually depends on the label of data, which not only consumes a lot of time for data labeling, but also is difficult to apply to data sets that cannot be given a label. In order to solve the labeling problem in anomaly series detection, an anomaly series detection algorithm based on segmentation feature representation is proposed. This method uses the idea of piecewise aggregation to standardize the calculation of time series, and obtains the characteristic representation of time series data, which can improve the reliability of anomaly detection of unlabeled time series. The expressed features are divided into abnormal series related features and irrelevant features. Pruning abnormal series irrelevant features can reduce the adverse impact of these features on the detection results. In order to effectively quantify the differences between different series, a time series similarity measurement method for time weight analysis is proposed, and the similarity matrix of time series is constructed to calculate the similarity between series, which can be applied to unlabeled time series. On this basis, the anomaly score of each sub-series is calculated according to the similarity matrix, which is used to determine the abnormal sub-series. Finally, the experimental comparison between synthetic data sets and real data sets shows that this method saves the computational overhead, improves the time efficiency of the algorithm and the accuracy of anomaly series detection.The supervised anomaly detection method of time series usually depends on the label of data, which not only consumes a lot of time for data labeling, but also is difficult to apply to data sets that cannot be given a label. In order to solve the labeling problem in anomaly series detection, an anomaly series detection algorithm based on segmentation feature representation is proposed. This method uses the idea of piecewise aggregation to standardize the calculation of time series, and obtains the characteristic representation of time series data, which can improve the reliability of anomaly detection of unlabeled time series. The expressed features are divided into abnormal series related features and irrelevant features. Pruning abnormal series irrelevant features can reduce the adverse impact of these features on the detection results. In order to effectively quantify the differences between different series, a time series similarity measurement method for time weight analysis is proposed, and the similarity matrix of time series is constructed to calculate the similarity between series, which can be applied to unlabeled time series. On this basis, the anomaly score of each sub-series is calculated according to the similarity matrix, which is used to determine the abnormal sub-series. Finally, the experimental comparison between synthetic data sets and real data sets shows that this method saves the computational overhead, improves the time efficiency of the algorithm and the accuracy of anomaly series detection. Reference | Related Articles | Metrics Select Research on Group Preference Fusion Strategy Based on Two-Layer Attention Mechanism MEI Yuzhu, HU Zhulin, ZHU Xinjuan Computer Engineering and Applications    2023, 59 (9): 272-279.   DOI: 10.3778/j.issn.1002-8331.2205-0437 Abstract （40）      PDF（pc） （532KB）（15）       Knowledge map    Save One of the current research hotspots of recommender systems and its evolution trend is that personalized recommendation gradually shifts from focusing on individual recommendation to focusing on group recommendation. At present, most group recommendation methods are accustomed to adopting a predefined static strategy when choosing a preference fusion strategy, and the characteristics of the static strategy make the algorithm unable to maximize the simulation of the real process of group decision-making. On the basis of previous research, this paper proposes a group recommendation method based on a two-layer attention mechanism, which fully takes into account the differences and mutual influence of group users, as well as the decision-making power in different fields. The attention weight of each member in the group is calculated to other members, the group member feature vector is obtained, and then the attention weight of each member in selecting a certain item is calculated, and the preference vector for the item for the group is generated. The interaction between group users and the process of group decision-making are fully restored. By comparing the CAMRa2011 and Meetup datasets with COM, SIG, AGR, AGREE, FastGR and other methods under different parameter conditions, the two indicators of normalized discount cumulative gain and hit rate are higher than the baseline model, is up to 0.025 4 and 0.030 7. Reference | Related Articles | Metrics Select Traffic Flow Forecasting Model for Improved Spatio-Temporal Transformer GAO Rong, WAN Yiliang, SHAO Xiongkai, Wu Xinyun Computer Engineering and Applications    2023, 59 (7): 250-260.   DOI: 10.3778/j.issn.1002-8331.2203-0290 Abstract （70）      PDF（pc） （844KB）（31）       Knowledge map    Save To address the low performance problem of traffic flow prediction model based on spatio-temporal Transformer model, an improved spatio-temporal Transformer model（ISTTM） based on encoder-decoder is proposed. The encoder encodes the historical traffic features and the decoder predicts the future sequences. Firstly, the encoder combines spatial sparse self-attentiveness and temporal hierarchical diffusion convolution to capture the dynamic spatial correlation and local spatial features of traffic flows, and then uses temporal self-attentiveness to model the nonlinear temporal correlation. Then, the decoder mines the spatio-temporal features of the input sequences similarly to the encoder. Finally, based on the spatio-temporal features extracted by the encoder-decoder, the impact of historical traffic observations on future forecasts is simulated using double cross-attention, modeling the direct relationship between each historical time step and each future time step and the impact on the whole future time period, and the final representation of the future traffic flow is output. To confirm the effectiveness of ISTTM, experiments are executed on two real-world large-scale datasets, METR-LA and NE-BJ, and the ISTTM results outperform the six state-of-the-art baselines. Reference | Related Articles | Metrics Select Application of CEEMDAN-HURST Algorithm in COVID-19 Prediction WANG Qiyun, ZHENG Zhongtuan Computer Engineering and Applications    2023, 59 (7): 261-268.   DOI: 10.3778/j.issn.1002-8331.2205-0253 Abstract （48）      PDF（pc） （696KB）（10）       Knowledge map    Save Considering the new COVID-19 cases are a nonlinear and non-stationary time series, a combined COVID-19 prediction model based on CEEMDAN-HURST algorithm is proposed. Firstly, the time series of newly confirmed cases are decomposed into sub-series with different frequencies using the complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） algorithm. Secondly, the randomness of each sub-sequence is analyzed by HURST index and the sub-sequence is integrated into three sub-sequences of high frequency, medium frequency and low frequency. The three sub-sequences are predicted by the least square support vector machine（LSSVM）. Finally, the prediction results of each reconstructed subsequence are superimposed to obtain the final predicted value of newly confirmed COVID-19 cases. The results show that the COVID-19 new case combination prediction model based on CEEMDAN-HURST algorithm improves the efficiency and prediction accuracy in the nonlinear time series prediction process. Compared with the CEEMDAN-PE combined model, the mean absolute error and root mean square error are reduced by 11.13% and 29.67%, respectively, indicating that the CEEMDAN-HURST algorithm can effectively solve the problems of low prediction efficiency and low prediction accuracy commonly existed in nonlinear time series forecasting models. Meanwhile, HURST index measures the deviation degree of time series, and the HURST index is introduced to merge, reconstruct and integrate, which can reduce the number of sub-series needed for time series prediction. Reference | Related Articles | Metrics Select Parallel OPTICS by Using Mean Distance and Relevance Marks ZHENG Jian, YU Xin Computer Engineering and Applications    2023, 59 (5): 232-244.   DOI: 10.3778/j.issn.1002-8331.2203-0018 Abstract （39）      PDF（pc） （901KB）（17）       Knowledge map    Save The main target of this paper is to design a parallel optics algorithm by using mean distance and relevance marks based on MapReduce, noted as POMDRM-MR, to deal with the problems of unreasonable data division, low accuracy of clustering results, the results are greatly affected by parameters and low efficiency of parallelization in parallel density-based clustering algorithm in big data. In POMDRM-MR, an approach called partition with reduced boundary points based on dimension sparsity（DS-PRBP） is proposed to divide the dataset. For each partition, the algorithm called marking and ordering points to identify the cluster structure（MOPTICS） is proposed to construct the correlations between data points and core points and mark the number of iterations, the field mean distance strategy（FMD） is proposed to calculate the field mean distance of data points instead of the reachable distance in measuring distance. After outputting sequence, combined with reordering and extracting clusters algorithm（REC）, the sequence is sorted twice which improves the accuracy and stability. In merging global clusters, an approach called using boundary density to filter local cluster（BD-FLC） is used to calculate and filter local clusters with similar density. And based on the union-type merging of n-ary trees and MapReduce, the parallel local cluster merging algorithm（MCNT-MR） is proposed to get the clustering results faster and merge local clusters in parallel which improves efficiency of merging local clusters. The experiments show that POMDRM-MR algorithm has better effect, and better parallelization performance on large-scale datasets. Reference | Related Articles | Metrics Select Hierarchical Grid Division to Realize Cluster and Scatter Visualization of Massive Map Markers FU Chen’en, CHEN Qiong Computer Engineering and Applications    2023, 59 (5): 245-251.   DOI: 10.3778/j.issn.1002-8331.2203-0123 Abstract （34）      PDF（pc） （688KB）（17）       Knowledge map    Save In traditional map visualization, point clustering is used in the display of massive map markers, but all kinds of point clustering algorithms are run-time calculations without hierarchical mechanism, and there is no filtering mechanism for map markers stack when a large number of points are scattered and displayed. In response to this problem, a solution of clustering and scattering of massive map markers is proposed based on hierarchical grid division. This method builds a K-D tree index for the center point of the hierarchical grid, and builds a quadtree index for the massive points. Through the index and storage technology, the efficient query of clustering is realized. Add grid filtering to eliminate stacking problems when massive points are scattered. The comparison is carried out on the experimental case data set, and the results show that, compared with the traditional point clustering scheme, the computing performance is significantly improved in the case of a large amount of data, and the filtering algorithm is added to the scattered display of massive markers, which effectively improves the user experience. Reference | Related Articles | Metrics Select Group Recommendation Method Integrating Probability Matrix Decomposition and ER Rules WANG Yonggui, ZHANG Jian Computer Engineering and Applications    2023, 59 (5): 252-261.   DOI: 10.3778/j.issn.1002-8331.2203-0125 Abstract （45）      PDF（pc） （595KB）（20）       Knowledge map    Save Group recommendation needs to consider the preferences of all members in a group at the same time, and recommend items to the group by integrating member preferences. Most of the existing researches on group recommendation methods assign the same weight to all users in the group, without considering the importance and reliability of different group members in real life. To solve this problem, a group recommendation method（FPMF-ER） based on probabilistic matrix decomposition and evidence reasoning rules is proposed to improve the process of individual prediction and preference fusion in group recommendation. Firstly, the classical probability matrix decomposition is improved by combining user relationship information to obtain a more complete and accurate personal prediction score. Subsequently, ER rule is introduced in the process of preference fusion of group members, and the influence of group members is identified according to the weight and reliability of group members, so that preference fusion is more reasonable and accurate. In order to verify the effectiveness of the method, a comparative experiment is conducted on the Book-Crossing dataset. The experimental results show that compared with the optimal benchmark model, the accuracy of FPMF-ER recommendation results and user satisfaction are increased by at least 2.55% and 2.06%, respectively. Reference | Related Articles | Metrics Select Node Importance Analysis Integrated with Community Assessment SUN Baibing, SUN Jiazheng, HE Quan, DU Yanhui Computer Engineering and Applications    2023, 59 (3): 226-233.   DOI: 10.3778/j.issn.1002-8331.2205-0509 Abstract （61）      PDF（pc） （701KB）（23）       Knowledge map    Save The research on mining key members in the target is an important branch in the field of social networks, but existing importance algorithms are prone to the phenomenon of aggregation of mined key nodes. To address this problem, a node importance algorithm integrated with community assessment is proposed, which defines a community importance assessment function based on the network topology of the target group, incorporating the internal influence of members in their community and external connectivity to comprehensively evaluate the importance of members. Taking four different complex networks as experimental data, compared with the existing algorithms, it is verified from three dimensions：propagation ability, robustness and Kendall correlation coefficient. The experiments show that the algorithm is more accurate in measuring the importance of members in the group. Reference | Related Articles | Metrics Select Construction Method of SDN for “Virtual-Real Fusion” Network Under Cloud CHEN Yu, HAN Jiujiang, LIU Jian, XIAN Ming, WANG Huimei, ZHANG Yuxiang Computer Engineering and Applications    2023, 59 (3): 234-243.   DOI: 10.3778/j.issn.1002-8331.2204-0465 Abstract （52）      PDF（pc） （4559KB）（27）       Knowledge map    Save In the scenario construction of network simulation test, it is difficult for some complicated physical terminals to realize virtual simulation, so it is essential for some physical terminals to be connected. However, the existing access modes are not applicable to large-scale network scenarios due to complex deployment and network performance bottlenecks. In order to effectively solve these problems, this paper proposes a construction method of virtual-real fusion network simulation test based on SDN（software-defined-network）. SDN controller combined with OpenFlow construction algorithm is used to realize the link management and data connectivity of virtual and real networks. It designs a prototype system for virtual-real fusion network simulation. The SDN controller is used to realize the common networking of virtual instances in the cloud environment and the physical terminals outside the cloud, so as to build a network simulation with “combination of virtual-real”. The experiments show that the network simulation method based on SDN can unite the virtual equipment in the cloud and the real terminals outside the cloud to make large-scale network efficiently, and do well in network performance. Reference | Related Articles | Metrics Select Self-Supervised Graph Neural Networks for Session-Based Recommendation WANG Yonggui, ZHAO Xiaoxuan Computer Engineering and Applications    2023, 59 (3): 244-252.   DOI: 10.3778/j.issn.1002-8331.2204-0245 Abstract （54）      PDF（pc） （600KB）（24）       Knowledge map    Save Compared with traditional recommendation methods, session-based recommendation methods are more susceptible to the data sparsity problem due to the limited data of short-term user interaction. In order to enhance session data and alleviate the impact of datas parseness on session recommendation performance, a model named self-supervised graph neural networks for session-based recommendation（Ss-GNN） is proposed. It constructs asession graph and establishes a session recommendation task based on graph attention network to obtain the item-level and session-level representation. Then, from the perspective of the session-level representation, it uses the user’s general and current interests to construct an auxiliary task to obtain self-supervised signals.It utilizes self-supervised learning to maximize mutual information between the recommendation task and the auxiliary task to enhance session data and improve recommendation performance. Experiments are conducted on two public data sets, Yoochoose and Tmall. Compared with the baseline model, the proposed model improves at least 0.94% and 0.79% in P@20 and MRR@20 on Yoochoose, 9.61% and 4.67% in P@20 and MRR@20 on Tmall, which proves the effectiveness of Ss-GNN model. Reference | Related Articles | Metrics Select A-DDPG：Research on Offloading of Multi-User Edge Computing System CAO Shaohua, JIANG Jiajia, CHEN Shu, ZHAN Zijun, ZHANG Weishan Computer Engineering and Applications    2023, 59 (1): 259-268.   DOI: 10.3778/j.issn.1002-8331.2203-0461 Abstract （87）      PDF（pc） （1276KB）（38）       Knowledge map    Save In order to reduce the total cost of users in multi-user systems with multiple edge servers, a deep reinforcement learning offloading algorithm（A-DDPG） based on DDPG is proposed by combining deep deterministic policy gradient（DDPG）, long short term memory（LSTM） and attention mechanism, which uses binary offloading strategy and takes into account the latency sensitivity of tasks and the limited server load as well as task migration to adapt offload tasks to minimize the total loss caused by latency-sensitive task timeouts. Two metrics, latency and energy consumption, are considered and different weight values are set to address the unfairness caused by different user types, and the task offloading problem is formulated to minimize the total cost of all task completion latency and energy consumption, with the selection of target servers and the amount of data offloaded as learning objectives. The experimental results show that the A-DDPG algorithm has good stability and convergence, and the total user cost of the A-DDPG algorithm is reduced by 27% and 26.66% compared to the DDPG algorithm and the twin delayed deep deterministic policy gradient（TD3） algorithm respectively. It achieves better results in terms of reward, total cost and task failure rate, as the average time to reach the optimal task failure rate is 57.14% and 40% earlier, respectively. Reference | Related Articles | Metrics Select Decentralized Federated Learning Strategy for Non-Independent and Identically Distributed Data TAN Rongjie, HONG Zhiyong, YU Wenhua, ZENG Zhiqiang Computer Engineering and Applications    2023, 59 (1): 269-277.   DOI: 10.3778/j.issn.1002-8331.2204-0165 Abstract （63）      PDF（pc） （4322KB）（47）       Knowledge map    Save Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized federated learning has the problem of performance degradation caused by non-IID distribution of training data. In order to solve this problem, the paper firstly proposes a calculation method of model similarity, and then designs a decentralized federated learning strategy based on the similarity of the model. The learning strategy is tested using five federated learning tasks, using the CNN model to train the fashion-mnist dataset, the alexnet model to train the cifar10 dataset, the TextRnn model to train the THUsnews dataset, the Resnet18 model to train the SVHN dataset and the LSTM model to train the sentiment140 dataset. The experimental results show that the designed strategy performs decentralized federated learning under the non-IID data of the five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23?percentage points respectively. Reference | Related Articles | Metrics Select Path Discrimination Method for Service Fault Propagation in Cloud Computing Environment SI Jianzhe, JIANG Ying, LI Rongchen, CHEN Weiwei Computer Engineering and Applications    2022, 58 (23): 94-103.   DOI: 10.3778/j.issn.1002-8331.2203-0239 Abstract （55）      PDF（pc） （1068KB）（31）       Knowledge map    Save Since cloud computing services can be characterized by dynamic, complexity and diversity, it makes the interaction between different services more complicated. As service failures happen frequently in cloud computing environment and faults are constantly spreading, resulting in system failure. Most of the existing fault propagation analysis methods all have some problems, such as over reliance on the historical data, only measuring single fault propagation factor, and failing to model the dynamic change system structure. In order to solve these problems, a method of service path discrimination method for service fault propagation in cloud computing environment is proposed. Firstly, the service interaction diagram is dynamically established. Secondly, the structure of service interaction diagram is optimized and service relationship diagram is set up. Thirdly, the possibility of service failure is calculated by considering service operating and environmental conditions, and the fault service is determined. Finally, the influencing factors of service fault propagation are comprehensively analyzed, and the service fault propagation probability is calculated, and the path discrimination of service fault propagation is carried out. Experimental results show that this method can accurately determine the fault service and effectively distinguish the service fault propagation path. Reference | Related Articles | Metrics Select Top-k Collective Spatial Keyword Approximation Query MENG Xiangfu, WANG Dandan, ZHANG Xiaoyan, JIA Jianghao Computer Engineering and Applications    2022, 58 (23): 104-116.   DOI: 10.3778/j.issn.1002-8331.2201-0393 Abstract （44）      PDF（pc） （979KB）（23）       Knowledge map    Save In recent years, the scale of spatio-textual data with location and text information has been increasing rapidly, and the spatial keyword query technology against spatio-textual data has been widely studied and applied. Most of the existing spatial keyword query methods usually take a single spatial object as the basic unit of the query results. Recently, there are a few recent research work aiming to find a group of spatial object as the basic unit of the query results, this group of spatial objects jointly meet the requirements of the given spatial keyword query. However, such kind of methods does not consider the relationships（such as social correlations， textual similarity） between spatial objects in the group. To deal with this problem, this paper proposes a top-[k] collective spatial keyword approximate query method. First, an association rule-based social relationship evaluation method for spatial objects is proposed. Furthermore, it designs a scoring function which combines the location distances and social relationships of spatial objects within a group. Second, a VP-Tree based pruning strategy is proposed for quickly searching the local neighborhood of spatial objects. Last, the top-[k] spatial object groups are selected as the query result by leveraging the scoring function to calculate the score of candidate spatial object groups. The experimental results show that the proposed spatial object social relationship evaluation method can achieve high accuracy, the proposed pruning strategy has high execution efficiency, and the obtained top-[k]groups of spatial objects can meet user need and preferences well. Reference | Related Articles | Metrics Select Collaborative Computing Offloading Strategy in Mobile Edge Computing LI Shun, GE Haibo, LIU Linhuan, CHEN Xutao Computer Engineering and Applications    2022, 58 (21): 83-90.   DOI: 10.3778/j.issn.1002-8331.2201-0015 Abstract （84）      PDF（pc） （713KB）（60）       Knowledge map    Save Aiming at the problem of resource waste in the idle state of remote edge servers when a single edge server is uninstalled, this paper proposes an edge cloud collaborative computing offloading strategy based on an improved hybrid particle swarm algorithm（cross reorganization PSO, CRPSO）. In this offloading strategy, a model is established with the goal of minimizing the total cost of the system（the weighted sum of time delay and energy consumption）. In the particle swarm algorithm, the fitness is used to group the advantages and disadvantages of the particles, and the disadvantages are solved by introducing the crossover idea in the genetic algorithm. The group of particles is optimized, the particles in the original population are optimized by a two-layer screening mechanism, and the optimal unloading strategy of the task is achieved through algorithm iteration. The simulation results show that, compared with the Local-MEC algorithm, ECPSO algorithm and GCPSO algorithm, the proposed CRPSO algorithm has the smallest total system cost and the optimization effect is obvious. Reference | Related Articles | Metrics Select Exploring Message Passing Method Between Spark Tasks XIA Libin, LIU Xiaoyu, SUN Wei, JIANG Xiaowei, SUN Gongxing Computer Engineering and Applications    2022, 58 (21): 91-97.   DOI: 10.3778/j.issn.1002-8331.2112-0182 Abstract （71）      PDF（pc） （1201KB）（27）       Knowledge map    Save Engineering problems and scientific research are facing dual challenges of big data processing and high-performance computing tasks. Spark, a distributed processing framework based on in-memory computing technology, has been widely used in academia and industry. However, its MapReduce-like programming model fails to communicate between tasks, causing numerical algorithms in scientific computing cannot be efficiently implemented. In response to the above problems, a computing system is proposed in this paper that combines Spark in-memory computing model with MPI message passing, which takes full advantage of the fast speed of memory access and multiple high performance communication mechanisms of MPI. It can not only supplement the insufficient expressiveness of the Spark programming model, but also provide a data-oriented DAG computation method for MPI. Internal runtime environment and scheduling strategy of Spark are modified to seamlessly integrate MPI into Spark to provide a unified in-memory computing system for high-performance computing and big data processing tasks. The tests indicate that the performance of numerical computation and iterative algorithm is improved by at least 50% compared with Spark. Reference | Related Articles | Metrics Select Research on Service Quality Model for SDN-Enabled Cloud Computing Platform ZHENG Bing, LI Hua Computer Engineering and Applications    2022, 58 (21): 98-108.   DOI: 10.3778/j.issn.1002-8331.2111-0525 Abstract （58）      PDF（pc） （695KB）（33）       Knowledge map    Save The SDN-enabled cloud computing platform has gradually been used widely, and more and more attention has been paid to the quality evaluation of services provided by SDN-enabled cloud computing platform. Firstly, the general cloud service and the software-based characteristics of SDN-enabled cloud computing platform services are considered. From the perspectives of service providers and users, the design requirements of service quality model are analyzed, and the categorical dimensions of service quality model are obtained. Then, referring to SQuaRE international standard, the service quality model QM-SDNCCP of SDN-enabled cloud computing platform is established, and the calculation method of the metrics is given. Furthermore, the purpose of describing and quantifying the service quality of SDN-enabled cloud computing platform is achieved. Finally, the reliability analysis of QM-SDNCCP is carried out, which shows that the design of QM-SDNCCP model characteristics and sub-characteristics are reliable and consistent, it means QM-SDNCCP can be applied in the stages of technology selection, acceptance, operation and service management and also it can be used to guide on selection of cloud platform service and quality improvement by service providers and users. Reference | Related Articles | Metrics Select Recommendation Algorithm Integrating Collaborative Knowledge Graph and Optimizing Graph Attention Network TANG Hong, FAN Sen, TANG Fan Computer Engineering and Applications    2022, 58 (19): 98-106.   DOI: 10.3778/j.issn.1002-8331.2201-0183 Abstract （148）      PDF（pc） （809KB）（51）       Knowledge map    Save Considering the problems of data sparseness and high model complexity in recommendation algorithms, this paper proposes a recommendation model that integrates collaborative knowledge graphs and optimized graph attention networks. Firstly, the knowledge graph and the user-item interaction graph are combined into a collaborative knowledge graph and embedded into the optimized graph attention network model, which cannot only alleviate the data sparsity problem well, but also mine potential interests and high order relationship of users. Secondly, using an optimized graph convolutional network, by removing feature transformation and nonlinear activation modules, the model complexity can be greatly reduced without affecting the overall recommendation performance. Combined with the deviation-based attention mechanism, the deviation between the candidate item and the user’s real interest item can be sensed in time to improve the training efficiency of the model. Finally, simulation experiments are carried out on the Movielens dataset and the Double dataset, and it is concluded that the recommended performance and time complexity of the algorithm are effectively improved compared with the comparison algorithm. Reference | Related Articles | Metrics Select Financial Time Series Forecasting Method Based on Multi-Scale Features and Attention Mechanism ZHAN Xi, PAN Zhisong, LI Wei, ZHANG Yanyan, BAI Wei WANG Cailing Computer Engineering and Applications    2022, 58 (19): 107-115.   DOI: 10.3778/j.issn.1002-8331.2203-0138 Abstract （111）      PDF（pc） （1099KB）（59）       Knowledge map    Save Financial time series forecasting is a very important practical problem in the economic field. However, due to the noise and volatility of financial markets, the forecasting accuracy of current existing methods is not yet satisfactory. In order to improve the prediction accuracy of financial time series, a hybrid prediction model DCNN_LSTM_AT integrating dilated convolutional neural network（DCNN）, long short term memory（LSTM） and attention mechanism（AT） is proposed. The model consists of two parts：the first part contains a dilated convolutional neural network and an LSTM-based encoder, whose function is to extract effective information at different temporal scales in the original sequence data. The second part is composed of an LSTM decoder with an attention mechanism, its function is to filter the information extracted from the first part and use the filtered information to make predictions. Finally, the proposed model is tested on 3 index datasets and 3 individual stock datasets, and compared with other common benchmark models. The experimental results show that the model has better prediction accuracy and stability than other models. Reference | Related Articles | Metrics Select Performance Optimization of Blockchain Sharding System Combined with Deep Reinforcement Learning WEN Jianwei, YAO Bingbing, WAN Jianxiong, LI Leixiao Computer Engineering and Applications    2022, 58 (19): 116-123.   DOI: 10.3778/j.issn.1002-8331.2203-0142 Abstract （76）      PDF（pc） （613KB）（43）       Knowledge map    Save Improving the throughput of the blockchain system is one of the key issues for the widespread application of blockchain. In view of the above problems, the sharding technology is applied to the blockchain system, and the throughput of the blockchain is improved by making the blockchain process transactions in parallel. The blockchain shard selection problem is established as a Markov decision process（MDP）, and a blockchain shard optimal selection strategy（BDQSB） based on deep reinforcement learning（DRL） is designed. The adopted BDQSB algorithm overcomes the shortcomings of the traditional DRL algorithm with high behavior space dimension and slow neural network training. The simulation results show that the proposed method can effectively reduce the behavior space dimension and improve the throughput and scalability of blockchain processing transactions. Reference | Related Articles | Metrics Select Patent Knowledge Recommendation Service Based on Deep Learning LI Zhenyu, ZHAN Hongfei, YU Junhe, WANG Rui, DENG Huijun Computer Engineering and Applications    2022, 58 (15): 95-109.   DOI: 10.3778/j.issn.1002-8331.2107-0418 Abstract （92）      PDF（pc） （2052KB）（61）       Knowledge map    Save Patent contains the most complete design information in most domains, it can provide designers with valuable guidance in solving design problems. Aiming at the problem that the existing patent recommendation methods are difficult to effectively recommend cross-domain patents, a cross-domain patent knowledge recommendation method based on deep learning is proposed for the conceptual design of innovative products. The product function and knowledge demand situation are modeled, the design problem is standardized, and the design problem space is generated. A semi-supervised learning algorithm（TG-TCI） is proposed to automatically classify and label patent function information according to the function base, and use entity recognition algorithm（BERT-BiLSTM-CRF） to extract patent application scenario terms and technical terms, combined with international patent classifications（IPC） information is used to represent the function, context, technology and domain attributes of patents, thereby generating patent knowledge space. Find the required cross-domain patents through the functional base and knowledge context mapping from the design problem space to the patent knowledge space, cluster and evaluate them according to the technology and domain attributes, and select specific patents to stimulate the creativity of designers. An actual case is used to analyze and verify the feasibility and effectiveness of the patent knowledge recommendation model based on deep learning. Reference | Related Articles | Metrics Select Deep Convolutional Neural Network Algorithm Based on Feature Map in Big Data Environment MAO Yimin, ZHANG Ruipeng, GAO Bo Computer Engineering and Applications    2022, 58 (15): 110-116.   DOI: 10.3778/j.issn.1002-8331.2010-0081 Abstract （114）      PDF（pc） （709KB）（33）       Knowledge map    Save Aiming at problems such as excessive network redundant parameters, poor parameter optimization ability and low parallel efficiency exist in DCNN（deep convolutional neural network） algorithm under big data environment. In this paper, a deep convolutional neural network algorithm based on feature graph and parallel computational entropy is proposed. The algorithm is MR-FPDCNN（deep convolutional neural network algorithm based on feature graph and parallel computing entropy using MapRuduce）. The algorithm designs the FMPTL（feature map pruning based on Taylor loss） and the pre-training network to obtain the compressed DCNN, which effectively reduces the redundant parameters and also reduces the computational cost of DCNN training. This paper proposes the IFAS based on ISS, initializes DCNN parameters according to the “IFAS” algorithm, realizes the parallelization training of DCNN, and improves the optimization ability of network. In the Reduce phase, a DLBPCE（dynamic load balancing strategy based on parallel computing entropy） is proposed to obtain global training results, realizing fast uniform grouping of data and increasing the acceleration ratio of the parallel system. Experimental results show that this algorithm not only reduces the computational cost of DCNN training in big data environment, but also improves the parallelization performance of parallel system. Reference | Related Articles | Metrics Select Two-Level Multi-Access Edge Computing Energy-Saving Offloading Strategy for IoT DENG Yu, ZHAO Junhui, ZHANG Qingmiao Computer Engineering and Applications    2022, 58 (13): 94-101.   DOI: 10.3778/j.issn.1002-8331.2108-0069 Abstract （71）      PDF（pc） （924KB）（60）       Knowledge map    Save Multi-access edge computing（MEC） technology sinks computing and storage resources to the edge of the network, which can greatly improve the computing power and real-time performance of the Internet of things（IoT） system. However, MEC is often constrained by the growth of computing demand and energy constraints. Thus, efficient computing offloading and energy consumption optimization mechanism is an important research direction in MEC technology. To ensure the computing efficiency while maximizing the energy efficiency in the computing process, a two-level edge nodes（ENs） relay network model is proposed, and an optimal energy consumption algorithm（OECA） for joint optimization of computing resources and channel resources is designed. Firstly, the energy efficiency in MEC is modeled as a 0-1 knapsack problem. Secondly, the system adaptively selects the computing mode and allocates wireless channel resources, aiming at minimizing the overall energy consumption of the system. Finally, the OECA algorithm is simulated and verified in Python environment. The simulation results show that OECA can increase the network capacity by 18.3% and reduce the energy consumption by 13.1% compared with the offloading strategy algorithm based on directed acyclic graph algorithm（DAGA）. Reference | Related Articles | Metrics Select Hybrid Recommendation Algorithm Combining Multi-Semantic Trust and Global Knowledge WANG Yonggui, CAI Yongwang, WANG Yang Computer Engineering and Applications    2022, 58 (13): 102-111.   DOI: 10.3778/j.issn.1002-8331.2108-0328 Abstract （61）      PDF（pc） （949KB）（40）       Knowledge map    Save The problem of data sparsity generally exists in collaborative filtering system. The similarity measurement which only considers the local context information on the common rating items does not have high reliability. In order to solve the above problems, a hybrid recommendation algorithm MSTGK is proposed, which integrates multi-semantic trust and global knowledge. Firstly, the weighted heterogeneous information network（WHIN） is introduced to deal with the impact of rating data, social relations, user tags and item attributes on user trust through weighted meta path, and mine trust information with different semantics to alleviate the problem of data sparsity. Secondly, considering the influence of popularity of items and user preference on user similarity, they are used as weight factors to improve the JMSD similarity measure in order to improve the accuracy of similarity calculation. Finally, the user’s multi-semantic trust and global similarity are integrated for comprehensive recommendation. Experimental results on two real datasets, DoubanMovie and Yelp, show that the proposed method alleviates data sparsity and compared with other baseline methods, the prediction accuracy is improved by 2.01 and 2.45?percentage points respectively. Reference | Related Articles | Metrics Select Nonnegative Matrix Factorization Community Detection Method  Enhanced by Graph Convolutional Network ZHENG Yulong, CHEN Qimai, HE Chaobo, LIU Hai, ZHANG Xiaoyu Computer Engineering and Applications    2022, 58 (11): 73-83.   DOI: 10.3778/j.issn.1002-8331.2110-0487 Abstract （73）      PDF（pc） （1293KB）（50）       Knowledge map    Save Nonnegative matrix factorization（NMF） has been widely used in community detection due to its effectiveness and better interpretability. However, most existing NMF-based methods are linear, and consequently cannot effectively process the nonlinear characteristics of complex networks. As a result, the performance of community detection needs to be further improved. In view of this, NMFGCN is proposed, which is a nonlinear NMF-based method enhanced by graph convolutional network（GCN）. NMFGCN contains two main modules：GCN and NMF. GCN is applied to obtain the representation of nodes, where NMF takes the representation as input to generate community representation of a given network. In addition, a joint optimization method is proposed to train NMFGCN, which not only allows NMFGCN to possess nonlinear trait, but also enables its two modules to be optimized jointly. Extensive experiments are conducted on synthetic networks and real networks. The results show that NMFGCN is superior to state-of-the-art NMF-based methods, and thereby proves that NMFGCN indeed can boost the performances of NMF-based methods. Moreover, NMFGCN also outperforms some widely-used graph representation based methods, such as LINE and Deepwalk. Reference | Related Articles | Metrics Select Research on DBN Prediction Model of E-Commerce Customer Churn ZHOU Wanting, ZHAO Zhijie, LIU Yang, WANG Jiaying, HAN Xiaowei Computer Engineering and Applications    2022, 58 (11): 84-92.   DOI: 10.3778/j.issn.1002-8331.2104-0221 Abstract （113）      PDF（pc） （687KB）（41）       Knowledge map    Save With the rapid development of e-commerce and the rapid market share of enterprises, customers have become the core factor of competition among enterprises. Existing related studies are mostly devoted to analyzing the phenomenon of customer churn using full data input mode, and the differences caused by different types of customers need to be further explored. In view of the traditional RFM model can not accurately explain the reasons for e-commerce customer churn, this study divides customers into active and inactive clusters, and proposes an optimized RFM theoretical model and an empirical model of deep belief network to predict e-commerce customer churn. The results show that the influence intensity of different types of customer churn factors is different. For active users, the total amount of customer purchases is the main factor affecting customer churn; for inactive users, the longer the customer enters the store, the more likely it is to retain the customer. By analyzing the reasons why inactive users do not churn and active users churn, it can help enterprises formulate effective customer management strategies to attract potential customers and retain existing customers to the greatest extent, so as to obtain the most market benefits. Reference | Related Articles | Metrics Select Investigating Users’ News Browsing Behavior and Their Perceived Value LYU Liangchao, WU Xiaoyu, LI Yuan, MAO Jiaxin, WEN Jirong Computer Engineering and Applications    2022, 58 (9): 91-97.   DOI: 10.3778/j.issn.1002-8331.2108-0355 Abstract （70）      PDF（pc） （699KB）（26）       Knowledge map    Save With the development of Internet technology, increasing attention has been paid to the user information behavior research. Browsing behavior is an important part of user information behavior. Research on browsing behavior is critical for user experience. This paper focuses on the browsing behavior of Toutiao users. Based on the customer perceived value theory, a mixed method of combining interview and questionnaire survey is used to explore the factors influencing users’ behavior in browsing online news. The results show that when users browse news on the mobile device, only functional value has a significant positive impact on news browsing behavior. Since the Toutiao news platform is similar to other online news applications, the conclusion can be generalized. News creators and platform operators are suggested to focus on improving content quality and reducing useless information, as well as the authenticity and reliability of news content. Reference | Related Articles | Metrics Select Group Recommendation Method Combining Leader Influence and Implicit Trust Metrics WANG Yonggui, LIN Jiamin, HE Jiayu Computer Engineering and Applications    2022, 58 (9): 98-106.   DOI: 10.3778/j.issn.1002-8331.2107-0011 Abstract （159）      PDF（pc） （749KB）（28）       Knowledge map    Save Group recommendation needs to consider the preferences of all members of a group at the same time, and then integrate the preferences to recommend items to the group. Most of the existing researches on group recommendation methods use fixed and symmetric relationship weights to predict scores, ignoring the complex relationship influence among group members, which will lead to low accuracy of group recommendation. In order to solve the above problems, a group recommendation method（GRS-IT） is proposed, which integrates leader influence and implicit trust. By combining fuzzy C-means clustering with Pearson correlation, the method can find groups with high similarity, which can effectively improve the effect and stability of group recommendation. By introducing the method of leader influence, combining Pearson correlation and an implicit trust calculation, the leaders in the group are found out and the dynamic influence weights between leaders and members and between members are obtained to reduce the error rate of group recommendation. In addition, this method integrates the time function based on the human forgetting curve into the prediction of item rating, and gives different time weight values over time to the prediction score, which further improves the accuracy of group recommendation. Finally, a comparative experiment is used to verify the effectiveness of GRS-IT. The results show that, compared with other group recommendation methods on the Movielens100K data set, the recommendation results are significantly improved in terms of accuracy and group member satisfaction. Reference | Related Articles | Metrics Select Neighborhood Awareness Graph Neural Networks for Session-Based Recommendation HE Qianqian, SUN Jingyu, ZENG Yazhu Computer Engineering and Applications    2022, 58 (9): 107-115.   DOI: 10.3778/j.issn.1002-8331.2105-0241 Abstract （112）      PDF（pc） （674KB）（51）       Knowledge map    Save Graph neural network and its improved models show better performances in session-based recommendation.They convert session as graph structure, and capture item features from transformation relationship between items in graph structure. However, most of the models ignore that there may be useful informations between different sessions, which can support the prediction task. Therefore, it proposes neighborhood aware graph neural networks for session-based recommendation（NA-GNN）. It builds session layer graph and global neighborhood layer graph, and captures item representations from them. Next it uses the attention mechanism to aggregate item representations. So then, it combines the maximum mutual information between sessions into network training. Experiments on two real datasets show that the prediction accuracy is better than others：P@20 increased by 1.85% on Yoochoose and 7.19% on Diginetica; MRR@20 increased by 0.48% and 8.36%, which proves the model is effective and reasonable. Reference | Related Articles | Metrics Select Measuring Content and Performance of IPFS Network DING Bowen, XU Yuedong, WANG Liang Computer Engineering and Applications    2022, 58 (7): 97-105.   DOI: 10.3778/j.issn.1002-8331.2102-0122 Abstract （110）      PDF（pc） （945KB）（53）       Knowledge map    Save Interplanetary file system（IPFS） is a decentralized file system. Since it can serve as a storage solution for various blockchain systems, it has been used in many applications. However, there remains questions about IPFS itself, including what kinds of contents have been stored on the system, and what are the key factors that affect performance of IPFS. By implementing and deploying a crawler in the IPFS network, measurement is done about both the content and the performance aspects of IPFS. Content measurement results show that more than 80% of files stored on IPFS are less than 1?MB. The graph comprising files and directories has a degree distribution that approximately follows a power law. In performance measurement, the IPFS network is found to have around 15,000 peers, which forms a connectivity graph whose in-degree follows a power law distribution. Chunk size and number of chunks have a direct impact on performance. Considerable difference in DHT resolution time is found between private and public network modes. DHT resolution is expected to be faster in both modes if the file being resolved has more providers. Reference | Related Articles | Metrics Select Improved Parallel Deep Forest Algorithm Combining with Information Theory MAO Yimin, GENG Junhao, CHEN Liang Computer Engineering and Applications    2022, 58 (7): 106-115.   DOI: 10.3778/j.issn.1002-8331.2103-0019 Abstract （169）      PDF（pc） （1044KB）（42）       Knowledge map    Save Aiming at the problems of excessive redundancy and irrelevant features, multi-grained scanning imbalance and low parallelization efficiency in big data parallel deep forest algorithm, this paper proposes an improved parallel deep forest based on information theory, named IPDFIT. Firstly, a dimension reduction based on information theory is presented to reduce the dimensionality of the original data set. Secondly, an improved multi-grained scanning strategy IMGSS to ensure that each feature appears in the data subset with the same frequency. Finally, in order to improve the parallel efficiency of the deep forest algorithm, the sample weighting strategy is proposed to evaluate the sample according to the forest in the cascade. Based on the evaluate results, the algorithm selects samples with poor evaluation to enter the next layer of training. The experimental results show that the IPDFIT algorithm has a better classification results in a big data environment, especially for data sets with more features. Reference | Related Articles | Metrics Select Research on Near Neighbor Improved Recommendation Algorithm Based on Recognition Degree Correction LI Jianfeng, FENG Linhui, YU Tianyi Computer Engineering and Applications    2022, 58 (7): 116-121.   DOI: 10.3778/j.issn.1002-8331.2101-0519 Abstract （78）      PDF（pc） （994KB）（58）       Knowledge map    Save How to integrate various factors for providing users accurately with personalized products that is always a hot issues. A new near-neighbor improved algorithm is brought forward through adopting popularization and personalization recognition degree correction, which can mine the hidden information more efficiently. After shown by the experimental results, although the recall fluctuates slightly up and down on that correction algorithm relative to the traditional near-neighbor algorithm, a number of other evaluation indexes improve dramatically. The false positive rate and the depth decrease, meanwhile, the precision, F1 value and the lift increase totally. In addition, the receiver operating characteristic curve and the lifting curve both suggest that the improved algorithm has more significant recommending effects. Reference | Related Articles | Metrics Select Recommendation Algorithm Combining Knowledge Graph and Attention Mechanism TANG Hong, FAN Sen, TANG Fan , ZHU Longjiao Computer Engineering and Applications    2022, 58 (5): 94-103.   DOI: 10.3778/j.issn.1002-8331.2109-0126 Abstract （225）      PDF（pc） （1025KB）（147）       Knowledge map    Save In order to solve the problem of information overload, this paper proposes a recommendation model that combines knowledge graph and attention mechanism. First of all, in the model, embedding the knowledge graph as auxiliary information can alleviate the data sparseness and cold start problems of the traditional recommendation algorithm, and bring interpretability to the final recommendation result. Secondly, in order to improve the recommendation accuracy and capture the dynamic changes of user interests, combined with the neural network in deep learning and the attention mechanism to generate user-adaptive representations, plus dynamic factors to better capture user dynamic changes in interest, using multiple layers perceptron makes scoring predictions for items. Finally, the simulation verification is performed on the MovieLens-latest-small movie dataset and the Douban dataset. The results show that the model for TOP-K list recommendation has better recommendation performance than other algorithms. Reference | Related Articles | Metrics Select Hybrid Recommendation Algorithm Combining Wolf Colony Algorithm and Fuzzy Clustering WANG Yonggui, LI Xin Computer Engineering and Applications    2022, 58 (5): 104-111.   DOI: 10.3778/j.issn.1002-8331.2101-0387 Abstract （83）      PDF（pc） （673KB）（46）       Knowledge map    Save Aiming at the data sparsity problem of traditional collaborative filtering recommendation algorithm and the limitation of searching for similar users, a hybrid recommendation algorithm combining wolf colony algorithm and fuzzy clustering is proposed. Firstly, in the process of data processing, according to the project-based collaborative filtering algorithm, the data relationship between projects is fully mined, and the zero value of the original matrix is filled to reduce the data sparsity; secondly, from the user’s point of view, according to the size of the membership degree of fuzzy clustering, the relevant neighbor set is selected to expand the search scope of relevant users; the wolf colony algorithm is introduced into fuzzy clustering, with the help of fuzzy clustering, wolf colony algorithm has the advantage of global search to improve the accuracy of finding similar users. The experimental results on real datasets show that the proposed algorithm alleviates the problem of data sparsity, reduces the recommendation error significantly, and has a good recommendation effect compared with the traditional recommendation algorithm. Reference | Related Articles | Metrics Select Social Recommendation Algorithm Based on Attention Gated Neural Network QIU Ye, SHAO Xiongkai, GAO Rong, WANG Chunzhi, LI Jing Computer Engineering and Applications    2022, 58 (5): 112-118.   DOI: 10.3778/j.issn.1002-8331.2101-0516 Abstract （124）      PDF（pc） （695KB）（106）       Knowledge map    Save To address the problem of low recommendation accuracy in social recommendation algorithms, a multi-headed attention gating neural network（MAGN） algorithm is proposed. Specifically, a gated neural network is used to fuse the input user and the user’s friend pairs to obtain a joint embedding, then an attentional memory network is used to obtain the influence of different friends on the user in different aspects, and then multi-headed attention is used to obtain several friends who have a high degree of influence on the user in different aspects. Finally, a gated neural network is used to mix friend influence with the user’s own interest preferences, and items are then recommended based on mixed interest preferences. Experiments on two publicly real available datasets further validate the effectiveness of the proposed method. Reference | Related Articles | Metrics Select Collaborative Filtering Algorithm Combining Time-Weighted Trust and User Preferences ZHANG Qishan, ZHU Meng Computer Engineering and Applications    2022, 58 (3): 112-118.   DOI: 10.3778/j.issn.1002-8331.2012-0467 Abstract （84）      PDF（pc） （678KB）（45）       Knowledge map    Save Aiming at the problems of sparse rating data and dynamic changes of user interests in the existing collaborative filtering recommendation algorithms, a collaborative filtering algorithm combining time-weighted trust and user preferences is proposed. Considering the unevenness of user rating time, the time weight is improved and incorporated into the direct trust calculation to alleviate the problem of dynamic changes in user interest. Through the indirect trust obtained by trust transfer and the establishment of user preference matrix for item tags, the preference similarity between users is obtained to alleviate the sparseness of data. The user’s trust and preference similarity are combined for recommendation. Experimental results show that compared with other baseline algorithms, the proposed algorithm has a higher F1 value, which improves the quality of recommendation. Reference | Related Articles | Metrics Select Research on Sustained Cohesive Subgraph Search Algorithm in Large Temporal Graphs LI Yuan, LIU Jinsheng, ZHAO Huiqun, SUN Jing Computer Engineering and Applications    2022, 58 (3): 119-126.   DOI: 10.3778/j.issn.1002-8331.2012-0563 Abstract （83）      PDF（pc） （771KB）（28）       Knowledge map    Save Temporal graph is a kind of graph structure with timestamps on its edges, where the timestamps indicate the time when the edges appear, that is, the graph evolves constantly by time. The problem of cohesive subgraph mining for graph data has very strong practical significance. Recently, most of the existing work in temporal graphs focus on the problem of cohesive subgraph detection, which aims to find all the target subgraphs in the temporal graph. However, when the size of the temporal graph becoming too large, the problem of cohesive subgraph detection will go extremely impractical and ineffective. The purpose of this paper is to study the problem of cohesive subgraph search, which has been overlooked for long. Specifically, given a query vertex in the temporal graph, the goal is to find a cohesive subgraph that sustaining over a period and include the query vertex. That is the subgraph satisfies the time sustainability. To this end, two efficient searching algorithms are designed based on global reduction and local expansion to cope with different application scenarios. A large number of experiments on four real networks verify the efficiency of the proposed algorithms. Reference | Related Articles | Metrics Select Collaborative Filtering Recommendation Algorithm Based on Subspace Clustering WANG Yingbo, HAN Guomiao, WANG Mingze Computer Engineering and Applications    2022, 58 (3): 127-134.   DOI: 10.3778/j.issn.1002-8331.2101-0029 Abstract （115）      PDF（pc） （737KB）（48）       Knowledge map    Save In order to reduce the impact of data sparsity on the efficiency of the recommendation algorithm, a collaborative filtering recommendation algorithm based on subspace clustering（SCUCF） is proposed. The algorithm creates different subspaces of three types of evaluated items of interest, not interest, and neither interest nor interest. Using the project subspace to draw a neighbor user tree for the target user to find the neighbors of the target user. An improved user similarity calculation method is used to determine recommended users. The algorithm is verified by MovieLens 100K and MovieLens 1M data sets. Experimental results show that the algorithm can improve the recommendation performance of the recommendation algorithm. Moreover, compared with other similar improved algorithms, this algorithm also shows certain advantages. Reference | Related Articles | Metrics page Page 1 of 2 Total 46 records First page Prev page Next page Last page