基于流行的推荐研究综述

doi:10.3778/j.issn.1673-9418.2309016

摘要/Abstract

摘要： 目前，基于流行的推荐系统成为研究热点。流行度使得推荐效果得到显著提升，而流行偏差带来的马太效应也引发了研究者的广泛关注，同时一些研究者考虑将二者结合作为混合式流行来实现推荐。采用流行这一概念，对流行度、流行偏差和混合式流行进行统一表示。首先介绍流行在推荐领域的应用背景，然后根据不同视角，分别对流行度增强推荐方法、去流行偏差推荐方法和混合式流行推荐方法进行综述。在每类方法中，根据建模的具体子任务或具体策略进行进一步划分，对代表性方法进行分析介绍，评价其优点和局限性等，并详细总结每类方法的方法机制和适用场景，从多方面对不同方法间的联系与区别进行讨论。还介绍了该领域中常用数据集、评价指标和基线算法，并对其中代表性方法进行性能对比分析。最后针对基于流行的推荐研究发展趋势提出一些看法，从多角度对该技术未来的发展难点与热点进行总结与展望。

关键词: 流行度, 流行偏差, 混合式流行, 基于流行的推荐

Abstract: Currently, popularity based recommendation has become a research hotspot. The use of popularity considerably improves the recommendation effects, while the Matthew effect caused by popularity bias has also garnered extensive attention among researchers. Some researchers consider combining both aspects to produce hybrid popularity based recommendation. Adopting the concept of popularity, a unified representation of popularity, popularity bias, and hybrid popularity is provided in this paper. Firstly, the background of popularity in the field of recommendation is introduced. Then, based on different perspectives, a comprehensive survey on popularity-enhanced recommendation methods, popularity debias recommendation methods, and hybrid popularity based recommendation methods is provided. Each type of method is further subdivided in specific subtasks of modeling or concrete strategies. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The mechanisms and applicable scenarios of each method are also summarized in detail. Furthermore, the commonly used datasets, performance evaluation indicators and baseline are introduced. A comparative analysis of the representative methods performance is also listed. Finally, some opinions on the trends of popularity based recommendation are presented. An outlook on the technical difficulties and hotspots for future development from multiple perspectives is analyzed and predicted.

雷钦岚, 田萱. 基于流行的推荐研究综述[J]. 计算机科学与探索, 2024, 18(5): 1109-1134.

LEI Qinlan, TIAN Xuan. Survey on Popularity Based Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(5): 1109-1134.

参考文献

[1] 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 learning based news recommendation algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 971-998.
[2] WU C, WU F, AN M, et al. Neural news recommendation with attentive multi-view learning[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019. Menlo Park: AAAI, 2019: 3863-3869.
[3] 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.
[4] MA Y, MAO J, BA Z, et al. Location recommendation by combining geographical, categorical, and social preferences with location popularity[J]. Information Processing and Management, 2020, 57(4): 102-115.
[5] LIN F, JIANG W, ZHANG J, et al. Dynamic popularity-aware contrastive learning for recommendation[C]//Proceedings of the 2021 Asian Conference on Machine Learning, Nov 17-19, 2021: 964-968.
[6] SHEN Q, TAO W, ZHANG J, et al. SAR-Net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Queensland, Nov 1-5, 2021. New York: ACM, 2021: 4094-4103.
[7] WEI T, FENG F, CHEN J, et al. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore, Aug 14-18, 2021. New York: ACM, 2021: 1791-1800.
[8] ZHANG Y, FENG F, HE X, et al. Causal intervention for leveraging popularity bias in recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 11-20.
[9] ZHAO Z, CHEN J, ZHOU S, et al. Popularity bias is not always evil: disentangling benign and harmful bias for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 9920-9931.
[10] 丁琪, 田萱, 孙国栋. 基于注意力增强的热点感知新闻推荐模型[J]. 电子学报, 2023, 51(1): 93-104.
DING Q, TIAN X, SUN G D. HAN: hotspot-aware attention enhanced news recommendation[J]. Acta Electronica Sinica, 2023, 51(1): 93-104.
[11] QI T, WU F, WU C, et al. PP-Rec: news recommendation with personalized user interest and time-aware news popularity[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.
[12] GE Y, ZHAO S, ZHOU H, et al. Understanding echo chambers in ecommerce recommender systems[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 2261-2270.
[13] MEHROTRA R, MCINERNEY J, BOUCHARD H, et al. Towards a fair marketplace: counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 2243-2251.
[14] CANAMARES R, CASTELLS P. Should I follow the crowd? A probabilistic analysis of the effectiveness of popularity in recommender systems[C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, Jul 8-12, 2018. New York: ACM, 2018: 415-424.
[15] PERC M. The Matthew effect in empirical data[J]. Journal of the Royal Society Interface, 2014, 11(98): 369-378.
[16] STECK H. Calibrated recommendations[C]//Proceedings of the 12th ACM Conference on Recommender Systems, Vancouver, Oct 2-7, 2018. New York: ACM, 2018: 154-162.
[17] ZHU Z, HE Y, ZHAO X, et al. Popularity bias in dynamic recommendation[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore, Aug 14-18, 2021. New York: ACM, 2021: 2439-2449.
[18] JI Y, SUN A, ZHANG J, et al. A re-visit of the popularity baseline in recommender systems[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 1749-1752.
[19] ELAHI M, KHOLGH D K, KIAROSTAMI M S, et al. Investigating the impact of recommender systems on user-based and item-based popularity bias[J]. Information Processing and Management, 2021, 58(5): 971-998.
[20] FU Z, XIAN Y, GENG S, et al. Popcorn: human-in-the-loop popularity debiasing in conversational recommender systems[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Queensland, Nov 1-5, 2021. New York: ACM, 2021: 494-503.
[21] ABDOLLAHPOURI H, MANSOURY M, BURKE R, et al. The connection between popularity bias, calibration, and fairness in recommendation[C]//Proceedings of the 14th ACM Conference on Recommender Systems, Brazil, Sep 22-26, 2020. New York: ACM, 2020: 726-731.
[22] ZHANG Y, CHENG D Z, YAO T, et al. A model of two tales: dual transfer learning framework for improved long-tail item recommendation[C]//Proceedings of the Web Conference 2021, Slovenia, Apr 19-23, 2021. New York: ACM, 2021: 2220-2231.
[23] LI J, LU K, HUANG Z, et al. On both cold-start and long-tail recommendation with social data[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(1): 194-208.
[24] PARK Y J, TUZHILIN A. The long tail of recommender systems and how to leverage it[C]//Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, Oct 23-25, 2008. New York: ACM, 2008: 11-18.
[25] ANELLI V W, NOIA T D, SCIASCIO E D, et al. Local popularity and time in top-N recommendation[C]//LNCS 11437: Proceedings of the 41st European Conference on IR Research: Advances in Information Retrieval, Cologne, Apr 14-18, 2019. Cham: Springer, 2019: 861-868.
[26] YU D, SHEN Y, XU K, et al. Context-specific point-of-interest recommendation based on popularity-weighted random sampling and factorization machine[J]. ISPRS International Journal of Geo-Information, 2021, 10(4): 249-258.
[27] ZHONG C, ZHU J, XI H. PS-LSTM: popularity analysis and social network for point-of-interest recommendation in previously unvisited locations[C]//Proceedings of the 2nd International Conference on Computing, Networks and Internet of Things, Beijing, May 20-22, 2021. New York: ACM, 2021: 28-36.
[28] LAI C H, LEE S J, HUANG H L. A social recommendation method based on the integration of social relationship and product popularity[J]. International Journal of Human-Computer Studies, 2019, 12(2): 42-57.
[29] LIU H, KOU H, YAN C, et al. Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph[J]. Complex, 2020, 15(3): 698-709.
[30] ABDOLLAHPOURI H, BURKE R, MOBASHER B. Controlling popularity bias in learning-to-rank recommendation[C]//Proceedings of the 11th ACM Conference on Recommender Systems, Como, Aug 27-31, 2017. New York: ACM, 2017: 42-46.
[31] ZHU Z, HE Y, ZHAO X, et al. Popularity-opportunity bias in collaborative filtering[C]//Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Israel, Mar 8-12, 2021. New York: ACM, 2021: 85-93.
[32] CHEN Z, XIAO R, LI C, et al. ESAM: discriminative domain adaptation with non-displayed items to improve long-tail performance[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 579-588.
[33] BORATTO L, FENU G, MARRAS M. Connecting user and item perspectives in popularity debiasing for collaborative recommendation[J]. Information Processing and Management, 2021, 58(1): 102-113.
[34] XV G, LIN C, LI H, et al. Neutralizing popularity bias in recommendation models[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: 2623-2628.
[35] RHEE W, CHO S M, SUH B. Countering popularity bias by regularizing score differences[C]//Proceedings of the 16th ACM Conference on Recommender Systems, Seattle, Sep 1-23, 2022. New York: ACM, 2022: 145-155.
[36] LIU Y, CAO Q, SHEN H, et al. Popularity debiasing from exposure to interaction in collaborative filtering[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, China, Jul 23-27, 2023. New York: ACM, 2023: 1801-1805.
[37] ANTIKACIOGLU A, RAVI R. Post processing recommender systems for diversity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Madrid, Jul 11-15, 2022. New York: ACM, 2017: 707-716.
[38] ZEHLIKE M, BONCHI F, CASTILLO C, et al. FA*IR: a fair top-k ranking algorithm[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 1569-1578.
[39] ABDOLLAHPOURI H, BURKE R, MOBASHER B. Managing popularity bias in recommender systems with personalized re-ranking[C]//Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, Sarasota, May 19-22, 2019. Menlo Park: AAAI, 2019: 413-418.
[40] ABDOLLAHPOURI H, MANSOURY M, BURKE R, et al. User-centered evaluation of popularity bias in recommender systems[C]//Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht, Jun 21-25, 2021. New York: ACM, 2021: 119-129.
[41] ZHANG F, SHEN Q. A model-agnostic popularity debias training framework for click-through rate prediction in recommender system[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, China, Jul 23-27, 2023. New York: ACM, 2023: 1760-1764.
[42] YALCIN E, BILGE A. Investigating and counteracting popularity bias in group recommendations[J]. Information Processing and Management, 2021, 58(5): 102-109.
[43] LIANG D, CHARLIN L, MCINERNEY J, et al. Modeling user exposure in recommendation[C]//Proceedings of the 25th International Conference on World Wide Web, Montreal, Apr 11-15, 2016. New York: ACM, 2016: 951-961.
[44] WANG W, FENG F, HE X, et al. Deconfounded recommendation for alleviating bias amplification[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore, Aug 14-18, 2021. New York: ACM, 2021: 1717-1725.
[45] 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, Ljubljana, Apr 19-23, 2021. New York: ACM, 2021: 2980-2991.
[46] ZHAO W, TANG D, CHEN X, et al. Disentangled causal embedding with contrastive learning for recommender system[C]//Proceedings of the ACM Web Conference 2023, Austin, Apr 30-May 4, 2023. New York: ACM, 2023: 406-410.
[47] LIU Q, TIAN F, ZHENG Q, et al. Disentangling interest and conformity for eliminating popularity bias in session-based recommendation[J]. Knowledge and Information Systems, 2023, 65(6): 2645-2664.
[48] GUPTA P, SHARMA A, MALHOTRA P, et al. CauSeR: causal session-based recommendations for handling popularity bias[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Queensland, Nov 1-5, 2021. New York: ACM, 2021: 3048-3052.
[49] HE M, LI C, HU X, et al. Mitigating popularity bias in recommendation via counterfactual inference[C]//Proceedings of the 27th International Conference Database Systems for Advanced Applications, Apr 11-14, 2022. Cham: Springer, 2022: 377-388.
[50] SCHNABEL T, SWAMINATHAN A, SINGH A, et al. Recommendations as treatments: debiasing learning and evaluation[C]//Proceedings of the 33rd International Conference on Machine Learning, New York, Jun 19-24, 2016: 1670-1679.
[51] BOTTOU L, PETERS J, QUINONERO C J, et al. Counterfactual reasoning and learning systems: the example of computational advertising[J]. The Journal of Machine Learning Research, 2013, 14(1): 3207-3260.
[52] GRUSON A, CHANDAR P, CHARBUILLET C, et al. Offline evaluation to make decisions about playlist recommendation algorithms[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Feb 11-15, 2019. New York: ACM, 2019: 420-428.
[53] YANG L, CUI Y, XUAN Y, et al. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback[C]//Proceedings of the 12th ACM Conference on Recommender Systems, Vancouver, Oct 2-7, 2018. New York: ACM, 2018: 279-287.
[54] HUANG J, Oosterhuis H, DE R M, et al. Keeping dataset biases out of the simulation: a debiased simulator for reinforcement learning based recommender systems[C]//Proceedings of the 14th ACM Conference on Recommender Systems, Brazil, Sep 22-26, 2020. New York: ACM, 2020: 190-199.
[55] SAITO Y, YAGINUMA S, NISHINO Y, et al. Unbiased recommender learning from missing-not-at-random implicit feedback[C]//Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, Feb 3-7, 2020. New York: ACM, 2020: 501-509.
[56] GAO X, JOI Q, MI Z, et al. Similarity measure based on punishing popular items for collaborative filtering[C]//Proceedings of the 2018 International Conference on Computer, Information and Telecommunication Systems, Alsace, Jul 11-13, 2018. Piscataway: IEEE, 2018: 1-5.
[57] ZHANG X, SU K, QIAN F, et al. Collaborative filtering algorithm based on item popularity and dynamic changes of interest[C]//Modern Management Based on Big Data III - Proceedings of MMBD 2022, Seoul, Aug 15-18, 2022. Amsterdam: IOS Press, 2022: 132-140.
[58] KRISHNAN A, SHARMA A, SANKAR A, et al. An adversarial approach to improve long-tail performance in neural collaborative filtering[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 1491-1494.
[59] 张帅, 高旻, 文俊浩, 等. 基于自监督学习的去流行度偏差推荐方法[J]. 电子学报, 2022, 50(10): 2361-2371.
ZHANG S, GAO M, WEN J H, et al. Self-supervised learning for alleviating popularity bias in recommender systems[J]. Acta Electronica Sinica, 2022, 50(10): 2361-2371.
[60] BONNER S, VASILE F. Causal embeddings for recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems, Macao, China, Aug 10-16, 2019. New York: ACM, 2018: 104-112.
[61] LIU Z, MEI S, XIONG C, et al. Text matching improves sequential recommendation by reducing popularity biases[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, Oct 21-25, 2023. New York: ACM, 2023: 1534-1544.
[62] CHEN J, DONG H, WANG X, et al. Bias and debias in recommender system: a survey and future directions[J]. ACM Transactions on Information Systems, 2023, 41(3): 57-67.
[63] TAKACS G, TIKK D. Alternating least squares for personalized ranking[C]//Proceedings of the 6th ACM Conference on Recommender Systems, Dublin, Sep 9-13, 2012. New York: ACM, 2012: 83-90.
[64] WASILEWSKI J, HURLEY N. Incorporating diversity in a learning to rank recommender system[C]//Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference, Florida, May 16-18, 2016. Menlo Park: AAAI, 2016: 572-578.
[65] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Arlington, Jun 18-21, 2009. Virginia: AUAI Press, 2009: 452-461.
[66] LI Y, CHEN H, FU Z, et al. User-oriented fairness in recommendation[C]//Proceedings of the Web Conference 2021, Slovenia, Apr 19-23, 2021. New York: ACM, 2021: 624-632.
[67] ABDOLLAHPOURI H. Popularity bias in ranking and recommendation[C]//Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, Honolulu, Jan 27-28, 2019. New York: ACM, 2019: 529-530.
[68] LIU Y, CAO X, YU Y. Are you influenced by others when rating? Improve rating prediction by conformity modeling[C]//Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Sep 15-19, 2016. New York: ACM, 2016: 269-272.
[69] BORGES R, STEFANIDIS K. On mitigating popularity bias in recommendations via variational autoencoders[C]//Proceedings of the 36th ACM/SIGAPP Symposium on Applied Computing, Mar 22-26, 2021. New York: ACM, 2021: 1383- 1389.
[70] MENA M E, CANAMARES R, CASTELLS P, et al. Popularity bias in false-positive metrics for recommender systems evaluation[J]. ACM Transactions on Information Systems, 2021, 39(3): 36-43.
[71] CHEN Z, WU J, LI C, et al. Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders[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: 60-69.
[72] 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, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 1835-1844.
[73] FENG S, CONG G, AN B, et al. POI2Vec: geographical latent representation for predicting future visitors[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, Feb 4-9, 2017. Menlo Park: AAAI, 2017: 11-17.
[74] GAO L, LI Y, LI R, et al. ST-RNet: a time-aware point-of-interest recommendation method based on neural network[C]//Proceedings of the 2019 International Joint Conference on Neural Networks, Budapest, Jul 14-19, 2019. Piscataway: IEEE, 2019: 1-8.
[75] YEHUDA K. Collaborative filtering with temporal dynamics[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, Jun 28-Jul 1, 2009. New York: ACM, 2009: 447-456.
[76] LESOTA O, MELCHIORRE A, REKABSAZ N, et al. Analyzing item popularity bias of music recommender systems: are different genders equally affected?[C]//Proceedings of the 15th ACM Conference on Recommender Systems, Amsterdam, Sep 27-Oct 1, 2021. New York: ACM, 2021: 601-606.
[77] WANG X, ZHANG Y, YAMASAKI T. Earn more social attention: user popularity based tag recommendation system[C]//Proceedings of the Web Conference 2020, Taipei, China, Jul 20-23, 2020. New York: ACM, 2020: 212-216.
[78] 高仰, 刘渊. 融合社交关系和知识图谱的推荐算法[J]. 计算机科学与探索, 2023, 17(1): 238-250.
GAO Y, LIU Y. Recommendation algorithm combining social relationship and knowledge graph[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 238-250.
[79] NAYAK A, GARG M, DUVVURU M R. News popularity beyond the click-through-rate for personalized recommendations[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, China, Jul 23-27, 2023. New York: ACM, 2023: 1396-1405.
[80] YALCIN E, BILGE A. Popularity bias in personality perspective: an analysis of how personality traits expose individuals to the unfair recommendation[J]. Concurrency and Computation: Practice and Experience, 2023, 35(9): 194-208.
[81] FERWERDA B, INGESSON E, BERNDL M, et al. I don’t care how popular you are! Investigating popularity bias in music recommendations from a user’s perspective[C]//Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, San Francisco, Mar 10-13, 2023. New York: ACM, 2023: 357-361.