融合文本主题的异构网络学术刊物推荐方法

doi:10.3778/j.issn.1002-8331.2207-0502

摘要/Abstract

摘要： 学术大数据的高速膨胀为学术工作者高效选择有效学术信息带来了巨大挑战，运用学术刊物推荐以应对学术信息过载是主流方式之一。此研究专门解决如何为论文手稿高效推荐合适投稿期刊这一问题。引入学术异构信息网络，融合论文文本主题信息，提出一种新的学术刊物推荐方法（SCVR）。借助主题模型建模论文摘要和标题等文本内容提取主题信息，指导不同类型节点映射到多主题特征空间；将元路径上下文信息聚合到目标节点，形成了多跳元路径下节点的多主题表示；将不同元路径下形成的节点向量进行融合，实现每个节点多元元路径下的多主题表示。SCVR利用节点文本内容和网络结构学习节点多主题表示，完成学术刊物推荐。在两个真实学术数据集上的测试发现，提出了一种基于异构信息网络且融合文本主题信息的学术刊物推荐方法，在相同条件下，SCVR的推荐效果比仅基于异构信息网络的推荐结果在Precision和NDCG上平均提高了2.7%，且比经典学术刊物推荐方法平均高了19%，说明SCVR在学术刊物推荐领域有更优良的性能。

关键词: 异构信息网络, 文本主题, 推荐系统, 图表示学习, 元路径

Abstract: 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.

Key words: heterogeneous information network, article topic, recommendation system, network representation learning, mate-path

王冰源, 刘柏嵩, 张雪垣, 钦蒋承, 董倩, 钱江波. 融合文本主题的异构网络学术刊物推荐方法[J]. 计算机工程与应用, 2023, 59(11): 241-250.

WANG Bingyuan, LIU Baisong, ZHANG Xueyuan, QIN Jiangcheng, DONG Qian, QIAN Jiangbo. Article Topic-Based Heterogeneous Information Network for Venue Recommendation[J]. Computer Engineering and Applications, 2023, 59(11): 241-250.

参考文献

[1] United Nations Conference on Trade and Development.Digital Economy Report 2021 Cross-border data flows and development：for whom the data flow[R].2021
[2] SHI C，LI Y T，ZHANG J W，et al.A survey of heterogeneous information network analysis[J].IEEE Transactions on Knowledge and Data Engineering，2016，29（1）：17-37.
[3] LOCATELLO F，BAUER S，LUCIC M，et al.Challenging common assumptions in the unsupervised learning of disentangled representations[C]//International Conference on Machine Learning，2019：4114-4124.
[4] ZHENG Y，GAO C，LI X，et al.Disentangling user interest and conformity for recommendation with causal embedding[C]//Proceedings of the Web Conference，2021：2980-2991.
[5] CRAWFORD E，PINEAU J.Spatially invariant unsupervised object detection with convolutional neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：3412-3420.
[6] KOMODAKIS N，GIDARIS S.Unsupervised representation learning by predicting image rotations[C]//International Conference on Learning Representations（ICLR），2018.
[7] LIU Y，WANG X，WU S，et al.Independence promoted graph disentangled networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：4916-4923.
[8] VERMA V，QU M，KAWAGUCHI K，et al.GraphMix： improved training of GNNs for semi-supervised learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2021：10024-10032.
[9] GAO C，ZHENG Y，LI N，et al.Graph neural networks for recommender systems：challenges，methods，and directions[J].arXiv：2109.12843，2021.
[10] WU Z，PAN S，CHEN F，et al.A comprehensive survey on graph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems，2020，32（1）：4-24.
[11] BERG R，KIPF T N，WELLING M.Graph convolutional matrix completion[J].arXiv：1706.02263，2017.
[12] WANG X，HE X，CAO Y，et al.KGAT：knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2019：950-958.
[13] WANG H，ZHAO M，XIE X，et al.Knowledge graph convolutional networks for recommender systems[C]//The World Wide Web Conference，2019：3307-3313.
[14] ZHENG L，LU C T，JIANG F，et al.Spectral collaborative filtering[C]//Proceedings of the 12th ACM Conference on Recommender Systems，2018：311-319.
[15] LIAO J，ZHOU W，LUO F，et al.SocialLGN：light graph convolution network for social recommendation[J].Information Sciences，2022，589：595-607.
[16] CHANG J，GAO C，ZHENG Y，et al.Sequential recommendation with graph neural networks[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval，2021：378-387.
[17] ZHANG X，ZHOU Y，WANG J，et al Personal interest attention graph neural networks for session-based recommendation[J].Entropy，2021，23（11）：1500.
[18] DONG Y，CHAWLA N V，SWAMI A.Metapath2vec： scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2017：135-144.
[19] FU T，LEE W C，LEI Z.Hin2vec：explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management，2017： 1797-1806.
[20] CHANG Y，CHEN C，HU W，et al.MEGNN：meta-path extracted graph neural network for heterogeneous graph representation learning[J].Knowledge-Based Systems，2022，235：107611.
[21] SHI C，ZHOU C，KONG X，et al.HeteRecom：a semantic-based recommendation system in heterogeneous networks[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2012：1552-1555.
[22] SHI C，KONG X，YU P S，et al.Relevance search in heterogeneous networks[C]//Proceedings of the 15th International Conference on Extending Database Technology，2012：180-191.
[23] LI H，WANG Y，LYU Z，et al.Multi-task learning for recommendation over heterogeneous information network[J].IEEE Transactions on Knowledge and Data Engineering，2022，34（2）：789-802.
[24] WANG J X.Meta-learning in natural and artificial intelligence[J].Current Opinion in Behavioral Sciences，2021，38：90-95.
[25] LU Y，FANG Y，SHI C.Meta-learning on heterogeneous information networks for cold-start recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2020：1563-1573.
[26] LEWALLEN L P，CRANE P B.Choosing a publication venue[J].Journal of Professional Nursing，2010，26（4）： 250-254.
[27] YANG Z，DAVISON B D.Venue recommendation：submitting your paper with style[C]//2012 11th International Conference on Machine Learning and Applications，2012：681-686.
[28] MEDVET E，BARTOLI A，PICCININ G.Publication venue recommendation based on paper abstract[C]//2014 IEEE 26th International Conference on Tools with Artificial Intelligence，2014：1004-1010.
[29] SANTOS A T，MENDON?A S.Do papers（really） match journals’“aims and scope”? a computational assessment of innovation studies[J].Scientometrics，2022：1-22.
[30] ALHOORI H，FURUTA R.Recommendation of scholarly venues based on dynamic user interests[J].Journal of Informetrics，2017，11（2）：553-563.
[31] ALSHAREEF A M，ALHAMID M F，EL SADDIK A.Academic venue recommendations based on similarity learning of an extended nearby citation network[J]. IEEE Access，2019，7：38813-38825.
[32] PRADHAN T，PAL S.CNAVER：a content and network-based academic venue recommender system[J].Knowledge-Based Systems，2020，189：105092.
[33] PRADHAN T，PAL S.A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity[J].Future Generation Computer Systems，2020，110：1139-1166.
[34] PRADHAN T，KUMAR P，PAL S.CLAVER：an integrated framework of convolutional layer，bidirectional LSTM with attention mechanism based scholarly venue recommendation[J]. Information Sciences，2021，559：212-235.
[35] PRADHAN T，GUPTA A，PAL S.HASVRec：a modularized hierarchical attention-based scholarly venue recommender system[J].Knowledge-Based Systems，2020，204：106181.
[36] CHEN Z，XIA F，JIANG H，et al.AVER：random walk based academic venue recommendation[C]//Proceedings of the 24th International Conference on World Wide Web，2015：579-584.
[37] YU S，LIU J，YANG Z，et al.PAVE：personalized academic venue recommendation exploiting co-publication networks[J].Journal of Network and Computer Applications，2018，104：38-47.
[38] SUN Y，HAN J，YAN X，et al.PathSim：meta path-based top-k similarity search in heterogeneous information networks[J].Proceedings of the VLDB Endowment，2011，4（11）：992-1003.
[39] HU B，SHI C，ZHAO W X，et al.Leveraging meta-path based context for top-n recommendation with a neural co-attention model[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2018：1531-1540.
[40] BLEI D M，NG A Y，JORDAN M I.Latent Dirichlet allocation[J].Journal of Machine Learning Research，2003，3：993-1022.
[41] FEI C J，LIU B S.Focused creawler based on LDA extended topic terms[J].Computer Applications and Software，2018，35（4）：49-54.
[42] SHUAI J，ZHANG K，WU L，et al.A review-aware graph contrastive learning framework for recommendation[J]. arXiv：2204.12063，2022.
[43] SHI C，HU B，ZHAO W X，et al.Heterogeneous infor-mation network embedding for recommendation[J].IEEE Transactions on Knowledge and Data Engineering，2018，31（2）：357-370.