Survey of Research on Personalized News Recommendation Approaches

doi:10.3778/j.issn.1673-9418.2303026

Abstract

Abstract: Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload. In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in improving the news reading experience of users. This paper aims to systematically summarize personalized news recommendation methods based on deep learning. Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors. Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized. On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph. Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction.

Key words: personalized news recommendation, deep learning, graph structure learning

摘要： 个性化新闻推荐是帮助用户获取其感兴趣的新闻信息和缓解信息过载的重要技术。近年来，随着信息技术和社会发展，个性化新闻推荐得到了日益广泛的研究，并在改善用户的新闻阅读体验方面取得了显著成功。对基于深度学习的个性化新闻推荐方法进行了系统性综述。首先，分类介绍了个性化新闻推荐方法并分析各自特点及影响因素；然后，给出了个性化新闻推荐的总体框架，并对基于深度学习的个性化新闻推荐方法进行了分析总结；在此基础上，重点综述了基于图结构学习的个性化新闻推荐方法，包括基于用户-新闻交互图、知识图谱和社交关系图的新闻推荐；最后，分析了当前个性化新闻推荐所面临的挑战，探讨了如何解决个性化新闻推荐系统中数据稀疏性、模型可解释性、推荐结果多样性和新闻隐私保护等问题，并在未来研究方向中展望了更具体可操作的研究思路和方法。

关键词: 个性化新闻推荐, 深度学习, 图结构学习

MENG Xiangfu, HUO Hongjin, ZHANG Xiaoyan, WANG Wanchun, ZHU Jinxia. Survey of Research on Personalized News Recommendation Approaches[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(12): 2840-2860.

孟祥福, 霍红锦, 张霄雁, 王琬淳, 朱金侠. 个性化新闻推荐方法研究综述[J]. 计算机科学与探索, 2023, 17(12): 2840-2860.

References

[1] LI M M, WANG L C. A survey on personalized news recom-mendation technology[J]. IEEE Access, 2019, 7: 145861-145879.
[2] DONG Y, LIU S, CHAI J P. Research of hybrid collaborative filtering algorithm based on news recommendation[C]//Pro-ceedings of the 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Datong, Oct 15-17, 2016. Piscataway: IEEE, 2016: 898-902.
[3] WANG C, BLEI D M. Collaborative topic modeling for recommending scientific articles[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, Aug 21-24, 2011. New York: ACM, 2011: 448-456.
[4] OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Aug 13-17, 2017. New York: ACM, 2017: 1933-1942.
[5] LIU J H, DOLAN P, PEDERSEN E R. Personalized news recommendation based on click behavior[C]//Proceedings of the 15th International Conference on Intelligent User Inter-faces, Hong Kong, China, Feb 7-10, 2010. New York: ACM, 2010: 31-40.
[6] BANSAL T, DAS M, BHATTACHARYYA C. Content driven user profiling for comment-worthy recommendations of news and blog articles[C]//Proceedings of the 9th ACM Confe-rence on Recommender Systems, Vienna, Sep 16-20, 2015. New York: ACM, 2015: 195-202.
[7] LU Z, DOU Z, LIAN J, et al. Content-based collaborative filtering for news topic recommendation[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 217-223.
[8] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436.
[9] 黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报, 2018, 41(7): 1619-1647.
HUANG L W, JIANG B T, LV S Y, et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Computers, 2018, 41(7): 1619-1647.
[10] 余力, 杜启翰, 岳博妍, 等. 基于强化学习的推荐研究综述[J]. 计算机科学, 2021, 48(10): 1-18.
YU L, DU Q H, YUE B Y, et al. Survey of reinforcement learning based recommender systems[J]. Computer Science, 2021, 48(10): 1-18.
[11] 田萱, 丁琪, 廖子慧, 等. 基于深度学习的新闻推荐算法研究综述[J]. 计算机科学与探索, 2021, 15(6): 971-998.
TIAN X, DING Q, LIAO Z H, et al. Survey on deep lear-ning based news recommendation algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 971-998.
[12] 王绍卿, 李鑫鑫, 孙福振, 等. 个性化新闻推荐技术研究综述[J]. 计算机科学与探索, 2020, 14(1): 18-29.
WANG S Q, LI X X, SUN F Z, et al. Survey of research on personalized news recommendation techniques[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(1): 18-29.
[13] 孟祥武, 陈诚, 张玉洁. 移动新闻推荐技术及其应用研究综述[J]. 计算机学报, 2016, 39(4): 685-703.
MENG X W, CHEN C, ZHANG Y J. A survey of mobile news recommend techniques and applications[J]. Chinese Journal of Computers, 2016, 39(4): 685-703.
[14] LIU R, PENG H, CHEN Y, et al. HyperNews: simultaneous news recommendation and active-time prediction via a double-task deep neural network[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence, Yokohama, Jul 11-17, 2020: 3487-3493.
[15] 袁仁进, 陈刚. 顾及事件地理位置的新闻推荐方法研究[J]. 计算机科学, 2018, 45(S2): 462-467.
YUAN R J, CHEN G. Research on news recommendation methods considering geographical location of news[J]. Computer Science, 2018, 45(S2): 462-467.
[16] CHEN C, THOMAS L, MENG X, et al. Location-aware news recommendation using deep localized semantic analysis[C]//LNCS 10177: Proceedings of the 22nd International Conference on Database Systems for Advanced Applications, Suzhou, Mar 27-30, 2017. Cham: Springer, 2017: 507-524.
[17] XU W, CHOW C Y, YIU M L, et al. MobiFeed: a location-aware news feed framework for moving users[J]. GeoInfor-matica, 2015, 19(3): 633-669.
[18] SARAVANAPRIYA M, SENTHILKUMAR R, SAKTHEE SWARAN J. Multi-label convolution neural network for personalized news recommendation based on social media mining[J]. Journal of Scientific and Industrial Research, 2022, 81(7): 785-797.
[19] ASHRAF M, TAHIR G A, ABRAR S. Personalized news recommendation based on multi-agent framework using social media preferences[C]//Proceedings of the 2018 International Conference on Smart Computing and Electronic Enterprise, Shah Alam, Jul 11-12, 2018. Piscataway: IEEE, 2018: 1-7.
[20] YANG J, WAN J, WANG Y, et al. Social network-based news recommendation with knowledge graph[C]//Proceedings of the 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, Chongqing, Nov 6-8, 2020. Piscataway: IEEE, 2020: 1255-1260.
[21] MOREIRA G, JANNACH D, CUNHA A. Contextual hybrid session-based news recommendation with recurrent neural networks[J]. IEEE Access, 2019, 7: 169185-169203.
[22] MENG L, SHI C. A context-aware interest drift network for session-based news recommendations[C]//Proceedings of the IEEE 6th International Conference on Computer and Commu-nications, Chengdu, Dec 11-14, 2020. Piscataway: IEEE, 2020: 1967-1971.
[23] SHEU H S, LI S. Context-aware graph embedding for session-based news recommendation[C]//Proceedings of the 14th ACM Conference on Recommender Systems, Brazil, Sep 22-26, 2020. New York: ACM, 2020: 657-662.
[24] GUO N, FU Z P, ZHAO Q H. Multimodal news recommen-dation based on deep reinforcement learning[C]//Proceedings of the 2022 International Conference on Intelligent Compu-ting and Signal Processing, Xi??an, Apr 15-17, 2022. New York: IEEE, 2022: 279-284.
[25] WU C, WU F, QI T, et al. MM-Rec: visiolinguistic model empowered multimodal news recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 2560-2564.
[26] XUN J H, ZHANG S Y, ZHAO Z, et al. Why do we click: visual impression-aware news recommendation[C]//Proceedings of the 2021 ACM Multimedia Conference, Oct 20-24, 2021. New York: ACM, 2021: 3881-3890.
[27] WU C H, WU F, QI T, et al. End-to-end learnable diversity-aware news recommendation[J]. arXiv:2204.00539, 2022.
[28] WU CH, WU F, QI T, et al. SentiRec: sentiment diversity-aware neural news recommendation[C]//Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Associa-tion for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, Suzhou, Dec 4-7, 2020. Stroudsburg: ACL, 2020: 44-53.
[29] QIN Z, ZHANG M. Research on news recommendation algorithm based on user interest and timeliness modeling[C]//Proceedings of the 2nd International Conference on Computing and Data Science, Stanford, Jan 28-30, 2021. New York: ACM, 2021: 1-6.
[30] WANG J, CHEN Y, WANG Z, et al. Popularity-enhanced news recommendation with multi-view interest representation[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Queensland, Nov 1-5, 2021. New York: ACM, 2021: 1949-1958.
[31] QI T, WU F, WU C, et al. PP-Rec: news recommendation with personalized user interest and time-aware news popu-larity[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Aug 1-6, 2021. Stroudsburg: ACL, 2021: 5457-5467.
[32] ZHU Q N, ZHOU X F, SONG Z L, et al. DAN: deep atten-tion neural network for news recommendation[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Con-ference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5973-5980.
[33] AN M X, WU F Z, WU C H, et al. Neural news recom-mendation with long- and short-term user representations[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics, Florence, Jul 28-Aug 2, 2019. Stroudsburg: ACL, 2019: 336-345.
[34] MENG L K, SHI C Y, HAO S F, et al. DCAN: deep co-attention network by modeling user preference and news lifecycle for news recommendation[C]//Proceedings of the 5th International Conference on Big Data Technologies, Qingdao, Sep 23-25, 2022. New York: ACM, 2021: 100-114.
[35] WU C H, WU F Z, GE S Y, et al. Neural news recommen-dation with multi-head self-attention[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 6388-6393.
[36] WU C H, WU F Z, AN M X, et al. NPA: neural news recom-mendation with personalized attention[C]//Proceedings of the 25th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 2576-2584.
[37] WU C H, WU F Z, QI T, et al. Two birds with one stone: unified model learning for both recall and ranking in news recommendation[C]//Findings of the Association for Compu-tational Linguistics, Dublin, May 22-27, 2022. Stroudsburg: ACL, 2022: 3474-3480.
[38] WU C H, WU F Z, AN M X, et al. Neural news recom-mendation with attentive multi-view learning[C]//Proceedings of the 28th International Joint Conference on Artificial Inte-lligence, Macao, China, Aug 10-16, 2019: 3863-3869.
[39] WU C H, WU F Z, QI T, et al. User modeling with click preference and reading satisfaction for news recommenda-tion[C]//Proceedings of the 29h International Joint Confe-rence on Artificial Intelligence, Yokohama, Jan 7-15, 2020: 3023-3029.
[40] DEVLIN J, CHANG M W, LEE K. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North Ame-rican Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Jun 2-7, 2019. Stroudsburg: ACL, 2019: 4171-4186.
[41] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st Annual Confe-rence on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 5998-6008.
[42] WU C H, WU F Z, Q I T, et al. Empowering news recom-mendation with pre-trained language models[C]//Proceedings of the 44th International ACM SIGIR Conference on Rese-arch and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 1652-1656.
[43] JIA Q L, LI J J, ZHANG Q, et al. RMBERT: news recom-mendation via recurrent reasoning memory network over BERT[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 1773-1777.
[44] ZHANG Q, LI J J, JIA Q L, et al. UNBERT: user-news matching BERT for news recommendation[C]//Proceedings of the 30th International Joint Conference on Artificial Inte-lligence, Montreal, Aug 19-27, 2021: 3356-3362.
[45] HUANG J S, HAN Z B, XU H Y, et al. Adapted transfor-mer network for news recommendation[J]. Neurocomputing, 2022, 469: 119-129.
[46] BI Q W, LI J, SHANG L F, et al. MTRec: multi-task lear-ning over BERT for news recommendation[C]//Proceedings of the 60th Annual Meeting of the Association-for-Compu-tational-Linguistics, Ireland, May 22-27, 2022. Stroudsburg: ACL, 2022: 2663-2669.
[47] ZHANG Q, JIA Q L, WANG C Y, et al. AMM: attentive multi-field matching for news recommendation[C]//Procee-dings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 1588-1592.
[48] KUMAR V, KHATTAR D, GUPTA S, et al. Deep neural architecture for news recommendation[C]//Proceedings of the Working Notes of CLEF 2017-Conference and Labs of the Evaluation Forum, Dublin, Sep 11-14, 2017: 1-19.
[49] REN L, CHEN D H, MIAO Z X, et al. News recommendation model based on long-term and short-term interests[C]//Proceedings of the 5th International Conference on Big Data Technologies, Qingdao, Sep 23-25, 2022. New York: ACM, 2022: 183-189.
[50] WU C H, WU F Z, AN M X, et al. Neural news recommen-dation with heterogeneous user behavior[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Con-ference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 4873-4882.
[51] WU C H, WU F Z, QI T, et al FeedRec: news feed recom-mendation with various user feedbacks[C]//Proceedings of the ACM Web Conference 2022, Lyon, Apr 25-29, 2022. New York: ACM, 2022: 2088-2097.
[52] WU C H, WU F Z, HUANG Y F, et al. Neural news recom-mendation with negative feedback[J]. CCF Transactions on Pervasive Computing and Interaction, 2020, 2(3): 178-188.
[53] GONG S, ZHU K Q. Positive, negative and neutral: modeling implicit feedback in session-based news recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1185-1195.
[54] HU Y F, QIU Z P, WU X. Denoising neural network for news recommendation with positive and negative implicit feedback[C]//Findings of the Association for Computational Linguistics, Seattle, Jul 10-15, 2022. Stroudsburg: ACL, 2022: 2320-2329.
[55] QI T, WU F Z, WU C H, et al. FUM: fine-grained and fast user modeling for news recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1974-1978.
[56] GE S, WU C, WU F. Graph enhanced representation learning for news recommendation[C]//Proceedings of the Web Con-ference 2020, Taipei, China, Apr 20-24, 2020. New York: ACM, 2020: 2863-2869.
[57] HU L, XU S, LI C, et al. Graph neural news recommen-dation with unsupervised preference disentanglement[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 4255-4264.
[58] HU L, LI C, SHI C. Graph neural news recommendation with long-term and short-term interest modeling[J]. Infor-mation Processing and Management, 2020, 57(2): 102142.
[59] JI Z, WU M, YANG H. Temporal sensitive heterogeneous graph neural network for news recommendation[J]. Future Generation Computer Systems, 2021, 125: 324-333.
[60] WU C H, WU F Z, HUANG Y F, et al. User-as-Graph: user modeling with heterogeneous graph pooling for news recom-mendation[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Aug 19-27, 2021: 1624-1630.
[61] MAO Z, LI J Y, WANG H, et al. DIGAT: modeling news recommendation with dual-graph interaction[C]//Findings of the Association for Computational Linguistics, Abu Dhabi, Dec 7-11, 2022. Stroudsburg: ACL, 2022: 6595-6607.
[62] MA M, NA S, WANG H. The graph-based behavior-aware recommendation for interactive news[J]. Applied Intelligence, 2022, 52(2): 1913-1929.
[63] WANG H, ZHANG F, XIE X, et al. DKN: deep knowledge-aware network for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference on World Wide Web, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 1835-1844.
[64] REN Y, WANG X, PANG G, et al. Dual attention network based on knowledge graph for news recommendation[C]//LNCS 12937: Proceedings of the 16th International Conference on Wireless Algorithms, Systems, and Applications, Nanjing, Jun 25-27, 2021.Cham: Springer, 2021: 364-375.
[65] LEE D, OH B, SEO S. News recommendation with topic-enriched knowledge graphs[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Ireland, Oct 19-23, 2020. New York: ACM, 2020: 695-704.
[66] SUN Y, YI F, ZENG C, et al. A hybrid approach to news recommendation based on knowledge graph and long short-term user preferences[C]//Proceedings of the 2021 IEEE International Conference on Services Computing, Chicago, Sep 5-10, 2021. Piscataway: IEEE, 2021: 165-173.
[67] XU Y, CHEN B, ZHEN J, et al. NRKM: news recommenda-tion based on knowledge graph with multi-view learning[C]//Proceedings of the 3rd International Conference on Control, Robotics and Intelligent System, Aug 26-28, 2022. New York: ACM, 2022: 123-127.
[68] QI T, WU F, WU C, et al. Personalized news recommen-dation with knowledge-aware interactive matching[C]//Pro-ceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 61-70.
[69] QIU Z, HU Y, WU X. Graph neural news recommendation with user existing and potential interest modeling[J]. ACM Transactions on Knowledge Discovery from Data, 2022, 16(5): 1-17.
[70] TIAN Y, YANG Y, REN X, et al. Joint knowledge pruning and recurrent graph convolution for news recommendation[C]//Proceedings of the 44th International ACM SIGIR Con-ference on Research and Development in Information Ret-rieval, Jul 11-15, 2021. New York: ACM, 2021: 51-60.
[71] ZHU P, CHENG D, LUO S, et al. Si-News: integrating social information for news recommendation with attention-based graph convolutional network[J]. Neurocomputing, 2022, 494: 33-42.
[72] GULLA J A, ZHANG L, PENG L, et al. The Adressa dataset for news recommendation[C]//Proceedings of the 2017 Inter-national Conference on Web Intelligence, Leipzig, Aug 23-26, 2017. New York: ACM, 2017: 1042-1048.
[73] LERMAN K, GHOSH R. Information Contagion: an empirical study of the spread of news on Digg and Twitter social networks[C]//Proceedings of the 4th International Conference on Weblogs and Social Media, Washington, May 23-26, 2010. Menlo Park: AAAI, 2010: 166-176.
[74] KILLE B, HOPFGARTNER F, BRODT T, et al. The Plista dataset[C]//Proceedings of the 2013 International News Recom-mender Systems Workshop and Challenge, Hong Kong, China, Oct 13, 2013. New York: ACM, 2013: 16-23.
[75] WU F Z, QIAO Y, CHEN J H, et al. MIND: a large-scale dataset for news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 3597-3606.
[76] GABRILOVICH E, DUMAIS S T, HORVITZ E. Newsjunkie: providing personalized news feeds via analysis of information novelty[C]//Proceedings of the 2004 International Conference on World Wide Web, New York, May 17-20, 2004. New York: ACM, 2004: 482-490.
[77] ZHENG G, ZHANG F, ZHENG Z, et al. DRN: a deep rein-forcement learning framework for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference on World Wide Web, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 167-176.
[78] WU C, WU F, WANG X, et al. Fairness-aware news recom-mendation with decomposed adversarial learning[C]//Pro-ceedings of the 35th AAAI Conference on Artificial Intelli-gence, the 33rd Conference on Innovative Applications of Artificial Intelligence, the 11th Symposium on Educational Advances in Artificial Intelligence, Feb 2-9, 2021. Menlo Park: AAAI, 2021: 4462-4469.
[79] WANG H W, ZHANG F Z, WANG J, et al. RippleNet: pro-pagating user preferences on the knowledge graph for recom-mender systems[C]//Proceedings of the 27th ACM Inter-national Conference on Information and Knowledge Mana-gement, Torino, Oct 22-26, 2018. New York: ACM, 2018: 417-426.
[80] TAO Q, WU F Z, WU C H, et al. HieRec: hierarchical user interest modeling for personalized news recommendation[C]//Proceedings of the 59th Annual Meeting of the Asso-ciation for Computational Linguistics and the 11th Interna-tional Joint Conference on Natural Language Processing, Aug 1-6, 2021. Stroudsburg: ACL, 2021: 5446-5456.
[81] QI T, WU F, WU C, et al. Uni-FedRec: a unified privacy-preserving news recommendation framework for model trai-ning and online serving[C]//Findings of the Association for Computational Linguistics, Punta Cana, Nov 16-20, 2021. Stroudsburg: ACL, 2021: 1438-1448.
[82] YI J, WU F, WU C, et al. Efficient-FedRec: efficient fede-rated learning framework for privacy-preserving news recom-mendation[C]//Proceedings of the 2021 Conference on Empi-rical Methods in Natural Language Processing, Punta Cana, Nov 7-11, 2021. Stroudsburg: ACL, 2021: 2814-2824.