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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 Complementary Product Recommendation Using Graph Neural Network NI Weijun, JI Shujuan, LIANG Yongquan Computer Engineering and Applications    2024, 60 (10): 292-300.   DOI: 10.3778/j.issn.1002-8331.2304-0222 Abstract （9）      PDF（pc） （3709KB）（5）       Knowledge map    Save Complementary product recommendations can provide complementary matching products for the convenience of users. However, existing work using graph neural networks ignores the multimodal information of products, and the performance of multimodal models is affected when the modal information is missing. In addition, existing multimodal models simply concatenate the modalities, ignoring the connections between the modalities. For these reasons, a complementary product recommendation using graph neural network (CPRUG) model is proposed. The model combines the graph neural network with multimodal information to strengthen the product representation. Then, it uses the graph attention network to deal with the multimodal absence problem, maintain the performance of the model, and improve the robustness of the model. Finally, it uses the common attention mechanism and matrix factorized bilinear pooling method to fuse multimodal features and learn the complementary relationship of products. Experiments are conducted on the Amazon dataset, and the experimental results show that the model outperforms other baseline models. Reference | Related Articles | Metrics Select Multi-Device Edge Computing Offload Method in Hybrid Action Space ZHANG Ji, QI Guoliang, DUO Chunhong, GONG Wenwen Computer Engineering and Applications    2024, 60 (10): 301-310.   DOI: 10.3778/j.issn.1002-8331.2304-0194 Abstract （8）      PDF（pc） （4350KB）（4）       Knowledge map    Save In order to reduce the total cost of device-level in multi-device multi-edge server scenarios and solve the algorithm limitation of existing deep reinforcement learning (DRL) that only supports a single action space, a hybrid-based multi-agent deep determination policy gradient (H-MADDPG) is proposed. Firstly, the MEC system model is established by considering various complex environmental conditions, such as the dynamic change of computing power of IoT devices/servers with load, time-varying wireless transmission channel gain, unknown energy harvesting, and the uncertainty of task size. Then, the problem model is established with the minimum total cost of integrated delay and energy consumption in a continuous time slot as the optimization objective. Finally, the problem is delivered to H-MADDPG in the form of Markov decision process (MDP), which trains two parallel policy networks with the assistance of the value network, and outputs discrete server selection and continuous task offload rate. The experimental results show that the H-MADDPG method has good convergence and stability. From different perspectives, such as whether the computing tasks are intensive or delay sensitive, the overall system return of H-MADDPG is better than Local, OffLoad and DDPG. Compared with other methods, it can maintain greater system throughput under the demand of computationally intensive tasks. Reference | Related Articles | Metrics Select Active Microservice Fine-Grained Scaling Algorithm PENG Kai, MA Fangling, XU Bo, GUO Jialu, HU Menglan Computer Engineering and Applications    2024, 60 (8): 274-286.   DOI: 10.3778/j.issn.1002-8331.2303-0543 Abstract （17）      PDF（pc） （876KB）（25）       Knowledge map    Save Microservice architecture has become the basic service architecture of cloud data center. However, the existing studies on the elastic scaling scheme of microservice systems are mostly based on horizontal scaling at the service or instance level, ignoring the fine-grained vertical scaling that can make full use of single server resources, resulting in resource waste. Therefore, an active microservice fine-grained elastic scaling algorithm is designed in this paper. The algorithm forecasts the request arrival rate to preconfigure the system resources. Based on the predicted results, the square root staffing rule is applied to calculate the number of required resources, and then the microservice is scaled by using the fine-grained resource control feature of vertical scaling and the high availability of horizontal scaling. Finally, an instance migration algorithm based on microservice dependency is applied to further reduce the resource overhead. Experimental results show that the proposed algorithm is effective in optimizing the delay and overhead of microservice systems. Reference | Related Articles | Metrics Select Multi-View Group Recommendation Integrating Self-Attention and Graph Convolution WANG Yonggui, WANG Xinru Computer Engineering and Applications    2024, 60 (8): 287-295.   DOI: 10.3778/j.issn.1002-8331.2304-0035 Abstract （21）      PDF（pc） （604KB）（29）       Knowledge map    Save In order to solve the problem that most existing group recommendations only learn group representation from a single interaction between the group and the user, and that the fixed fusion strategy is difficult to dynamically adjust the weight. A multi-view group recommendation model (MVGR) is proposed, which integrates self-attention and graph convolution. Three different views, member level, item level and group level, are designed to capture high-level collaborative information among groups, users and items, alleviate the problem of data sparsity, and enhance group representation modeling. For item level views, the graph convolution neural network based on dichotomous graph is used to learn group preference vector and item embedding. MVGR further proposes an adaptive fusion component to dynamically adjust different view weights to get the final group preference vector. Experimental results on two real dataset show that the hit ratio (HR) and normalized discounted cumulative gain (NDCG) of the MVGR model are improved by an average of 8.89?percentage points and 1.56 percentage points on the Mafengwo dataset, and by an average of 2.79 percentage points and 2.7 percentage points on the CAMRa2011 dataset compared to the baseline model. Reference | Related Articles | Metrics Select Cooperative Task Processing in Multi-UAV Assisted Mobile Edge Computing CAO Huijuan, YU Genghua, CHEN Zhigang Computer Engineering and Applications    2024, 60 (4): 298-305.   DOI: 10.3778/j.issn.1002-8331.2301-0145 Abstract （59）      PDF（pc） （2281KB）（45）       Knowledge map    Save Mobile edge computing (MEC) is considered as a key technology to deal with computing intensive and delay sensitive tasks. However, in disaster response, emergency rescue and other scenarios, it is impossible to quickly deploy edge servers and provide services. Unmanned aerial vehicle (UAV) assisted MEC has attracted much attention because of its simple deployment and strong mobility. However, the computing resources and energy of UAV are limited, how to allocate resources becomes a difficult problem. To solve this problem, a strategy of efficient utilization resources with multi-UAV cooperation (LUAVs-Cor) is proposed. This strategy dynamically processes tasks through the multi-UAV cooperation. In order to make full use of computing resources of UAVs, the task transmission strategy is determined by searching for the optimal task combination. In addition, the number of UAVs dispatched is optimized according to the UAV processing capacity, the number of tasks and the processing status of tasks, which achieves dynamic deployment of UAVs and reduces energy consumption. By simulation experiments, it is concluded that the service capacity of LUAVs-Cor strategy has been increased by about 6.8%, and the overall energy consumption of UAV has been reduced by 10.3%. LUAVs-Cor strategy has a low collaboration cost and serves more users at a lower energy cost. Reference | Related Articles | Metrics Select Multi-Task Joint Learning for Graph Convolutional Neural Network Recommendations WANG Yonggui, ZOU Heyu Computer Engineering and Applications    2024, 60 (4): 306-314.   DOI: 10.3778/j.issn.1002-8331.2303-0508 Abstract （53）      PDF（pc） （2409KB）（39）       Knowledge map    Save Collaborative filtering recommendation based on graph neural network can mine the interaction information between users and items more effectively, but its performance is still affected by the problems of sparse data and low quality of representation learning. Therefore, a multi-task joint learning model for graph convolutional neural network recommendation (MTJL-GCN) is proposed. Firstly, the graph neural network is used to gather the homogeneous structural information on the user-item interaction graph and form the structural neighbor relationship with the initial embedding information, and the comparative learning assistance task of node neighbor relationship is designed to alleviate the data sparse problem. Secondly, random unified noise is added to the original representation of nodes to enhance the representation-level data, and a comparative learning assistance task is constructed for the node representation relationship. The learning objectives of alignment and uniformity are proposed to improve the quality of representation learning. Finally, the graph collaborative filtering recommendation task is combined with the comparative learning assistance task and the direct optimization of learning objectives for joint training, so as to improve the recommendation performance. Experiments on two public datasets of Amazon-books and Yelp2018 show that the model performs better than the baseline model in the two recommended performance indexes of Recall@k and NDCG@k, which proves the validity of the MTJL-GCN model. Reference | Related Articles | Metrics Select Recommendation Model Based on Time Aware and Interest Preference TANG Pan, WANG Xueming Computer Engineering and Applications    2023, 59 (24): 268-276.   DOI: 10.3778/j.issn.1002-8331.2212-0386 Abstract （48）      PDF（pc） （644KB）（32）       Knowledge map    Save To address the problem that traditional recommendation models cannot mine users’ fine-grained interest preferences, a recommendation model based on time aware and interest preferences（TAIP） is proposed. In TAIP model, the time interval information of user interaction is introduced into the sequence embedding matrix as auxiliary information, and a multi-scale temporal convolutional network with channel and spatial attention mechanisms is designed to accurately extract fine-grained short-term preferences. At the same time, the Transformer encoder is used to mine long-term preferences between target items and user interests. Finally, a fully connected network is used to achieve global feature fusion to provide recommendations. Experiments are conducted on the publicly available datasets MovieLens-1M and YELP. Compared with other models, TAIP model improves at least 4.84% and 1.38% in HR, NDCG and MRR, which has better recommendation performance and verifies the effectiveness of TAIP model. Reference | Related Articles | Metrics Select CodeBERT Based Code Classification Method CHENG Siqiang, LIU Jianxun, PENG Zhenlian, CAO Ben Computer Engineering and Applications    2023, 59 (24): 277-288.   DOI: 10.3778/j.issn.1002-8331.2209-0402 Abstract （77）      PDF（pc） （702KB）（63）       Knowledge map    Save With the continuous development of code big data, the amount of source code in the code base is gradually growing, which makes software code management more complex. How to quickly and effectively classify and manage the code in the code base is of great importance to the development of software engineering. The article introduces pre-trained models to code classification research for the first time and proposes an optimized code classification method, CBBCC, which firstly uses wordpiece to pre-process the source code. Secondly, a CodeBERT pre-training model is used to characterise the source code. Finally, the classification task is fine-tuned on the basis of the pre-trained model. To verify the effectiveness of the proposed model, experimental analysis is conducted on the POJ104 dataset. The experimental results show that the CBBCC model achieves more than 98% in all classification metrics compared to the seven benchmark models. The accuracy is improved by 1.1 percentage points over the current optimal model, reaching the SOTA value for the classification task on the POJ104 code classification dataset. CBBCC can effectively annotate code, improve the management of open source community source code and promote the development of the software engineering field. Reference | Related Articles | Metrics Select Domain Adversarial Adaptive Short-Term Workload Forecasting Model LIU Chunhong, JIAO Jie, WANG Jingxiong, LI Weili, ZHANG Junna Computer Engineering and Applications    2023, 59 (24): 289-297.   DOI: 10.3778/j.issn.1002-8331.2211-0036 Abstract （21）      PDF（pc） （817KB）（25）       Knowledge map    Save The accuracy of workload prediction is one of the main factors affecting the elastic resource management of cloud platforms. And there are a large number of short task workload sequences with insufficient historical information and unsmooth characteristics in the cloud, which makes it difficult to select appropriate models for accurate prediction. In this paper, a domain adversarial workload prediction model is proposed. The model uses SSA（singular spectrum analysis） to smooth the workload and solve the problem of irregularity. Similarity calculations are performed by combining MASS_V4（the fourth version of Mueen’s algorithm for similarity search） with temporal features to obtain suitable source-domain data-assisted migration prediction. The GRU（gated recurrent unit） is used as the reference to construct the network, a new loss function defined is with Y-discrepancy, and a prediction model is constructed with strong short-workload feature representation ability in the adversarial process. The proposed method is compared with other commonly used cloud workload prediction algorithms on two real cloud platform datasets and both show higher prediction accuracy. Reference | Related Articles | Metrics Select Multi-Server Collaborative Task Caching Strategy in Edge Computing MA Shixiong, GE Haibo, SONG Xing Computer Engineering and Applications    2023, 59 (20): 245-253.   DOI: 10.3778/j.issn.1002-8331.2211-0238 Abstract （68）      PDF（pc） （690KB）（58）       Knowledge map    Save Aiming at the contradiction between the limited computing and storage resources of the edge server and  a large number of user task requests, this paper designs an edge computing task cache network architecture based on multi-server collaboration. In this architecture, the edge server can cache and execute user tasks in memory. The cached tasks are executed on the cloud. Combining the characteristics of time-varying user task requests and users in adjacent areas tending to request similar tasks, a multi-server task caching algorithm（MSAC） based on improved Soft Actor-Critic is proposed. The algorithm aims at minimizing the user’s average task execution delay. In order to avoid repeatedly choosing the same action and converge to a local optimum, a maximum entropy model is introduced to encourage the edge server to explore the optimal action. By designing an experience sharing mechanism, the task caching strategy is optimized by collecting and learning the experience of local edge servers and adjacent servers. Simulation results show that compared with the highest popularity algorithm, independent SAC algorithm, DQN algorithm, and genetic algorithm, the proposed MSAC algorithm has the best effect in reducing the average execution delay of user tasks. Reference | Related Articles | Metrics Select Data Stream Classification Method Combining Micro-Clustering and Active Learning YIN Chunyong, CHEN Shuangshuang Computer Engineering and Applications    2023, 59 (20): 254-265.   DOI: 10.3778/j.issn.1002-8331.2210-0230 Abstract （30）      PDF（pc） （1058KB）（31）       Knowledge map    Save Data stream classification is an important research component in data mining, but the problems of concept drift and expensive labeling in data streams pose a great challenge to classification. Most of the existing research work adopts online classification technology based on active learning, which alleviates the problems of concept drift and limited labels to a certain extent. However, these methods are less efficient for classification and ignore the problem of memory overhead. Aiming at these problems, a data stream classification method combining micro-clustering and active learning is proposed（CALC）. Firstly, a new active learning hybrid query strategy is proposed to measure the importance of each microcluster during maintenance by combining it with error-based representative learning. Secondly, a set of microclusters is dynamically maintained to accommodate the concept drift generated in the data stream. In addition, an inert microcluster-based learning approach is used to achieve classification of the data stream and to accomplish online updates of the cached microclusters. Finally, comparative experiments are conducted using three real datasets and three simulated synthetic datasets, and the results show that CALC outperforms existing data stream classification algorithms in terms of classification accuracy and memory overhead. Compared with the benchmark model ORSL, the classification accuracy of CALC has been improved to a certain extent, and the average accuracy of the six data sets has been increased by 5.07, 2.41, 1.04, 1.03, 3.47 and 0.64 percentage points, respectively. Reference | Related Articles | Metrics Select Research of Metadata Management Method of Hierarchical Storage System Based on HDFS LIU Xiaoyu, XIA Libin, JIANG Xiaowei, SUN Gongxing Computer Engineering and Applications    2023, 59 (17): 257-265.   DOI: 10.3778/j.issn.1002-8331.2211-0230 Abstract （67）      PDF（pc） （5727KB）（32）       Knowledge map    Save With the continuous expansion of the scale of high-energy physics（HEP） experiments and the increase of experimental complexity, researchers are facing the challenge of big data storage. Considering the cost, energy consumption, storage cycle and maintenance management, the tape libraries with?large storage capacity and low cost have become an indispensable choice for mass storage systems in the field of HEP. However, HDFS heterogeneous storage doesn’t support tape library storage, it cannot meet the high cost performance requirements of storage system for the persistence and backup process of massive experimental data in Hadoop platform of HEP. In view of the above problems, in order to build an HDFS hierarchical storage system that supports disk-tape storage, the tape layer files can be seamlessly integrated in HDFS, and provide users with a unified file system namespace. In this research, it first overviews the existing methods of distributed file system metadata management, and further designs an improved one to realize the unified metadata management for HDFS hierarchical storage system. This method redesigns the file metadata structure in memory to build a unified memory directory tree, and implements access management and reliability assurance to achieve centralized and unified management of tape file metadata.?Test results show that the metadata server implements unified management of file metadata on heterogeneous resources and provides efficient metadata operation. The storage tiering system based on this method has high reliability. When reading and writing files of different sizes, the read and write throughput is better than that of the traditional storage tiering system EOSCTA in the HEP field. Reference | Related Articles | Metrics Select Incorporating Relational Awareness and Temporal Attention for Temporal Knowledge Graph Completion XU Zhihong, MAO Chen, WANG Liqin, DONG Yongfeng Computer Engineering and Applications    2023, 59 (17): 266-274.   DOI: 10.3778/j.issn.1002-8331.2210-0266 Abstract （68）      PDF（pc） （3433KB）（51）       Knowledge map    Save To address the problem that most of the existing temporal knowledge graph completion methods embed time information into triples and rely on static knowledge graph completion means to learn entity features, which cannot holistically consider structural information and temporal information in the graph. This paper proposes a temporal knowledge graph completion model incorporating relational awareness and temporal attention（RATA）. On the one hand, by introducing graph convolutional network with relation-aware aggregation mechanism to integrate entity and relation features, relation-specific parameters can enhance the expression ability of the message function, and encapsulate richer neighborhood context information. On the other hand, it employs the long short-term memory network incorporating self-attention mechanism to learn global and local features in the sequence. The experimental results on ICEWS18, ICEWS14, YAGO and WIKI show RATA generally outperforms the baseline model on MRR, Hits@1, Hits@3, and Hits@10, and has better advantages on large-scale temporal datasets. Reference | Related Articles | Metrics Select Design and Implementation of Tape Library Storage System Based on Microservice Architecture LIU Xiaoyu, XIA Libin, JIANG Xiaowei, SUN Gongxing Computer Engineering and Applications    2023, 59 (15): 253-263.   DOI: 10.3778/j.issn.1002-8331.2206-0067 Abstract （81）      PDF（pc） （777KB）（61）       Knowledge map    Save The establishment of HDFS tiered storage system with tape storage layer is an important part of improving Hadoop ecosystem in the field of high energy physics（HEP）. However, the traditional tape storage management system in HEP（such as Castor and CTA） doesn’t support HDFS. In addition, with the rapid growth of the amount of HEP data, the continuous development of current Internet technology and rapid changes in user needs, the traditional tape storage management system gradually presents the problem such as system expansion, load balancing, development and maintenance cost increases sharply. This paper designs and develops a tape storage management system based on microservice architecture. The system supports HDFS disk storage and distributes tape library resource management, file transfer, tape read/write and so on functions to each service instance in the form of microservices to disperse service pressure. Furthermore, aiming at the problem of low efficiency of traditional load algorithm, the system proposes and implements a load balancing algorithm based on server response factor. It sorts severs according to the server response factor calculated by user-defined parameters to ensure that user requests are scheduled to the server with the highest response factor for processing. ?Experimental results show that the tape library storage system in this paper can meet the requirements of HDFS file hierarchical storage. Compared with polling algorithm, the system performance of archiving based on server response factor is more than 6% better, and that of extraction is more than 64% better. ?Compared with random algorithm, archiving performance is more than 9% better, and extraction performance is more than 64% better. The application results of the system indicates that compared with the traditional system, the microservice architecture can decouple components and balance distributed loads in the system, and is more convenient in system development and maintenance. Reference | Related Articles | Metrics Select Session-Based Recommendation Based on Multi-Graph Neural Network Incorporating Social Information LEI Jingsheng, LI Ran, YANG Shengying, SHI Wenbin Computer Engineering and Applications    2023, 59 (15): 264-273.   DOI: 10.3778/j.issn.1002-8331.2209-0083 Abstract （51）      PDF（pc） （647KB）（35）       Knowledge map    Save In recommender systems, users’ interest in items is dynamic and influenced by various factors such as their own historical behavior and friends’ behavior. It has been a challenge for recommendation algorithms to jointly model users’ dynamic interests and social relationships. In this paper, the dynamic interests of users are explored by partitioning their behaviors into session sequences and modeling them as global graphs. After that, a social relationship graph based on users’ social relationships is constructed, and then the influence of users’ social relationships is captured through graph attention networks to dynamically determine the influence of each friend, and users’ dynamic interests are combined with friends’ social influence to obtain the final recommendation results. The algorithm is validated on Douban, Delicious and Yelp datasets. Compared with the most available baseline model, the algorithm improves more than 6 percentage points on the Douban dataset and more than 3 percentage points on the Delicious and Yelp datasets, which proves the effectiveness of the algorithm. Reference | Related Articles | Metrics Select Collaborative Filtering Recommendation Algorithm Based on Graph Convolution Attention Neural Network WANG Wei, DU Yuxuan, ZHENG Xiaoli, ZHANG Chuang Computer Engineering and Applications    2023, 59 (13): 247-258.   DOI: 10.3778/j.issn.1002-8331.2206-0190 Abstract （86）      PDF（pc） （826KB）（87）       Knowledge map    Save With the rapid iterative development of information technology, the problem of information overload is becoming more and more serious. The recommendation algorithm can solve the information overload to a certain extent, but the traditional recommendation algorithm can not effectively solve the related problems such as data sparsity and recommendation accuracy. This paper proposes a graph convolution attention collaborative filtering（GCACF） recommendation method. Firstly, the model obtains the relevant interactive information of users and projects and transforms into corresponding feature vectors. Secondly, the feature vector aggregates with the propagation of graph convolution neural network and the attention mechanism redistributes the aggregated weight coefficients. Finally, the BPR loss function optimizes aggregated eigenvector and the model obtains the final recommendation result. Through the comparative experiments on Movielens-1M and Amazon-baby on two public datasets, GCACF is superior to the baseline method in precision, recall, Mrr, hit and NDCG. Reference | Related Articles | Metrics Select Execution Plan Selection for Parallel Queries Using Graph Neural Networks TAO Wenxia, NIU Baoning, LIU Haonan Computer Engineering and Applications    2023, 59 (13): 259-265.   DOI: 10.3778/j.issn.1002-8331.2208-0300 Abstract （70）      PDF（pc） （521KB）（39）       Knowledge map    Save Queries constitute the largest proportion of workload of database systems（DBS）, and their efficiency affects the performance of DBS. The execution of a query is affected by other parallel queries, resulting in query interaction（QI）, which is the main factor that makes it difficult for query optimizers to select a good execution plan for parallel queries. An encoding scheme called features of plans based on operator（FPO） is proposed to represent execution plans. QI is reflected by data sharing and resource competition between operators. The plan selection model based on graph（PSG） is proposed. PSG takes operators as nodes, operator features as node features, and relations between operators as edges to generate heterogeneous graphs as inputs of the model. Considering that there are many kinds of relations between operators with different functions, relational graph convolutional network（RGCN） is used to aggregate information, obtain a graph representation of a query mix, and extract its QI. Through fully connected layers（FC）, an execution plan is selected for a query. The average accuracy of PSG is 47.3?percentage points higher than that of query optimizers in PostgreSQL. Reference | Related Articles | Metrics 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 （83）      PDF（pc） （782KB）（51）       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 （68）      PDF（pc） （749KB）（43）       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