Explainable Paper Recommendations Based on Heterogeneous Graph Representation Learning and the Attention Mechanism
Ma Xiao1, Deng Qiumiao1, Zhang Hongyu1, Wen Xuan1, Zeng Jiangfeng2
1.School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073 2.School of Information Management, Central China Normal University, Wuhan 430079
1 Jannach D, Zanker M, Felfernig A, et al. Recommender systems: an introduction[M]. Cambridge: Cambridge University Press, 2010. 2 Bai X M, Wang M Y, Lee I, et al. Scientific paper recommendation: a survey[J]. IEEE Access, 2019, 7: 9324-9339. 3 Ma S T, Zhang C Z, Liu X Z. A review of citation recommendation: from textual content to enriched context[J]. Scientometrics, 2020, 122(3): 1445-1472. 4 Sugiyama K, Kan M Y. Exploiting potential citation papers in scholarly paper recommendation[C]// Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. New York: ACM Press, 2013: 153-162. 5 杨辰, 郑若桢, 王楚涵, 等. 集成因子分解机及其在论文推荐中的应用研究[J]. 数据分析与知识发现, 2023, 7(8): 128-137. 6 Lu Y B, He Y, Cai Y X, et al. Time-aware neural collaborative filtering with multi-dimensional features on academic paper recommendation[C]// Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design. Piscataway: IEEE, 2021: 1052-1057. 7 Jeong C, Jang S, Park E, et al. A context-aware citation recommendation model with BERT and graph convolutional networks[J]. Scientometrics, 2020, 124(3): 1907-1922. 8 Shi H, Ma W, Zhang X L, et al. A hybrid paper recommendation method by using heterogeneous graph and metadata[C]// Proceedings of the 2020 International Joint Conference on Neural Networks. Piscataway: IEEE, 2020: 1-8. 9 Manju G, Abhinaya P, Hemalatha M R, et al. Cold start problem alleviation in a research paper recommendation system using the random walk approach on a heterogeneous user-paper graph[J]. International Journal of Intelligent Information Technologies, 2020, 16(2): 24-48. 10 Son J, Kim S B. Academic paper recommender system using multilevel simultaneous citation networks[J]. Decision Support Systems, 2018, 105: 24-33. 11 Dong Y X, 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. New York: ACM Press, 2017: 135-144. 12 Ma X, Zhang Y, Zeng J F. Newly published scientific papers recommendation in heterogeneous information networks[J]. Mobile Networks and Applications, 2019, 24(1): 69-79. 13 吴俊超, 刘柏嵩, 沈小烽, 等. 卷积融合文本和异质信息网络的学术论文推荐算法[J]. 计算机应用研究, 2022, 39(5): 1330-1336. 14 Ye L, Yang Y, Zeng J X. An interpretable mechanism for personalized recommendation based on cross feature[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(5): 9787-9798. 15 Zhang Y F, Chen X. Explainable recommendation: a survey and new perspectives[J]. Foundations and Trends? in Information Retrieval, 2020, 14(1): 1-101. 16 Wang N, Wang H N, Jia Y L, et al. Explainable recommendation via multi-task learning in opinionated text data[C]// Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. New York: ACM Press, 2018: 165-174. 17 Sparck Jones K, Walker S, Robertson S E. A probabilistic model of information retrieval: development and comparative experiments: Part 2[J]. Information Processing & Management, 2000, 36(6): 809-840. 18 李晓敏, 王昊, 李跃艳. 基于细粒度语义实体的学术论文推荐研究[J]. 情报科学, 2022, 40(4): 156-165. 19 Liu H F, Kong X J, Bai X M, et al. Context-based collaborative filtering for citation recommendation[J]. IEEE Access, 2015, 3: 1695-1703. 20 Jiang C, Ma X, Zeng J F, et al. TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences[J]. Scientometrics, 2023, 128(6): 3453-3471. 21 Chaudhuri A, Sarma M, Samanta D. SHARE: designing multiple criteria-based personalized research paper recommendation system[J]. Information Sciences, 2022, 617: 41-64. 22 Ma X, Deng Q M, Ye Y, et al. Attention based collaborator recommendation in heterogeneous academic networks[C]// Proceedings of the 2022 IEEE 25th International Conference on Computational Science and Engineering. Piscataway: IEEE, 2022: 51-58. 23 Zhang Y F, Lai G K, Zhang M, et al. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis[C]// Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. New York: ACM Press, 2014: 83-92. 24 Li P J, Wang Z H, Ren Z C, et al. Neural rating regression with abstractive tips generation for recommendation[C]// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2017: 345-354. 25 Dong L, Huang S H, Wei F R, et al. Learning to generate product reviews from attributes[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 623-632. 26 Tao Y Y, Jia Y L, Wang N, et al. The FacT: taming latent factor models for explainability with factorization trees[C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2019: 295-304. 27 Tai C Y, Huang L Y, Huang C K, et al. User-centric path reasoning towards explainable recommendation[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2021: 879-889. 28 Hu B 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. New York: ACM Press, 2018: 1531-1540. 29 乔连鹏, 侯会文, 王国仁. 属性公平的异质信息网络上的社区搜索算法[J]. 软件学报, 2023, 34(3): 1277-1291. 30 Wang X, Wang Y, Ling Y Z. Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 6275-6282. 31 Mou L L, Song Y P, Yan R, et al. Sequence to backward and forward sequences: a content-introducing approach to generative short-text conversation[C]// Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, 2016: 3349-3358. 32 Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization[OL]. (2015-02-19). http://arxiv.org/pdf/1409.2329. 33 Cho K, van Merri?nboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1724-1734. 34 See A, Liu P J, Manning C D. Get to the point: summarization with pointer-generator networks[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 1073-1083. 35 Li L, Zhang Y F, Chen L. Generate neural template explanations for recommendation[C]// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York: ACM Press, 2020: 755-764. 36 Lo K, Wang L L, Neumann M, et al. S2ORC: the semantic scholar open research corpus[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2020: 4969-4983. 37 Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]// Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Arlington: AUAI Press, 2009: 452-461. 38 Bahdanau D, Cho K H, Bengio Y. Neural machine translation by jointly learning to align and translate[OL]. (2016-05-19). https://arxiv.org/pdf/1409.0473. 39 Papineni K, Roukos S, Ward T, et al. BLEU: a method for automatic evaluation of machine translation[C]// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2002: 311-318. 40 Lin C Y. ROUGE: a package for automatic evaluation of summaries[C]// Proceedings of Workshop on Text Summarization Branches Out, Post-Conference Workshop of ACL 2004. Stroudsburg: Association for Computational Linguistics, 2004: 74-81. 责任编辑 冯家琪)