Survey on Diversified Recommendation

Peng Yingtao; Meng Xiaofeng; Du Zhijuan

doi:10.7544/issn1000-1239.202330600

Journal of Computer Research and Development > 2025 > 62(2): 285-313. > DOI: 10.7544/issn1000-1239.202330600 CSTR: 32373.14.issn1000-1239.202330600

Peng Yingtao, Meng Xiaofeng, Du Zhijuan. Survey on Diversified Recommendation[J]. Journal of Computer Research and Development, 2025, 62(2): 285-313. DOI: 10.7544/issn1000-1239.202330600

Citation:

Peng Yingtao, Meng Xiaofeng, Du Zhijuan. Survey on Diversified Recommendation[J]. Journal of Computer Research and Development, 2025, 62(2): 285-313. DOI: 10.7544/issn1000-1239.202330600

Citation:

Peng Yingtao, Meng Xiaofeng, Du Zhijuan. Survey on Diversified Recommendation[J]. Journal of Computer Research and Development, 2025, 62(2): 285-313. DOI: 10.7544/issn1000-1239.202330600

PDF (1815 KB)

Survey on Diversified Recommendation

1.
School of Information, Renmin University of China, Beijing 100872
2.
College of Computer Science, Inner Mongolia University, Hohhot 010021

Funds: This work was supported by the National Natural Science Foundation of China (62172423, 91846204, 62162048).

More Information

Author Bio:
Peng Yingtao: born in 1993. PhD candidate. Student member of CCF. His main research interests include recommender system, and knowledge graph and large language model

Meng Xiaofeng: born in 1964. PhD, professor, PhD supervisor. Fellow of CCF. His main research interests include database systems, data intelligence, data privacy and governance, and social computing

Du Zhijuan: born in 1986. PhD, associate professor. Member of CCF. Her main research interests include knowledge graph and big data management
Received Date: July 24, 2023
Revised Date: August 19, 2024
Available Online: December 11, 2024

Graphical Abstract

Abstract

Abstract

The recommender system has a significant role in alleviating information overload, allowing users to conveniently obtain products and services on various application platforms like Tmall, TikTok, and Xiaohongshu. However, most of the recommendation systems focus on the accuracy rate as the center, which leads to adverse effects such as the limitation of users’ vision, fewer display opportunities for some merchants, a single content ecosystem of the platform, and an unbalanced allocation of resources and information, such as triggering the filter bubble and the Matthew effect. As a result, strengthening the diversity of the recommendation system has become a key research point to fulfill the increasingly diversified material demands in people’s lives. In recent years, research on diversified recommendations has developed rapidly. However, this aspect needs to be more systematic in organization and summarization. This paper systematically reviews the issue of diversified recommendations within recommendation systems. Firstly, we put forward the problem definition, technical framework, classification, and application scenarios of diversified recommendations. Secondly, we make comparisons and analyses of models and algorithms from four perspectives. Subsequently, we summarize the commonly used datasets and metrics for diversified recommendations. Finally, we deliberate on the problems and challenges in this field to inspire future innovation and promote development.
- recommendation system,
- diversity,
- deep learning,
- balance,
- interest preference

FullText(HTML)

References (142)

References

[1]	He Ruining, McAuley J. VBPR: Visual bayesian personalized ranking from implicit feedback[C]//Proc of the 30th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2016: 144−150
[2]	Wu Chuhan, Wu Fangzhao, An Mingxiao, et al. NPA: Neural news recommendation with personalized attention[C]//Proc of the 25th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2019: 2576−2584
[3]	史存会,胡耀康,冯彬,等. 舆情场景下基于层次知识的话题推荐方法[J]. 计算机研究与发展,2021,58(8):1811−1819 doi: 10.7544/issn1000-1239.2021.20190749 Shi Cunhui, Hu Yaokang, Feng Bin, et al. A hierarchical knowledge based topic recommendation method in public opinion scenario[J]. Journal of Computer Research and Development, 2021, 58(8): 1811−1819 (in Chinese) doi: 10.7544/issn1000-1239.2021.20190749
[4]	Chen Jingyuan, Zhang Hanwang, He Xiangnan, et al. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention[C]//Proc of the 40th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2017: 335−344
[5]	Lian Jianxun, Zhou Xiaohuan, Zhang Fuzheng, et al. xdeepfm: Combining explicit and implicit feature interactions for recommender systems[C]//Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2018: 1754−1763
[6]	Zhou Guorui, Zhu Xiaoqiang, Song Chengru, et al. Deep interest network for click-through rate prediction[C]//Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2018: 1059−1068
[7]	Zhou Tao, Kuscsik Z, Liu Jianguo, et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J]. Proceedings of the National Academy of Sciences, 2010, 107(10): 4511−4515 doi: 10.1073/pnas.1000488107
[8]	Kunaver M, Požrl T. Diversity in recommender systems: A survey[J]. Knowledge-based systems, 2017, 123: 154−162 doi: 10.1016/j.knosys.2017.02.009
[9]	Wu Qiong, Liu Yong, Miao Chunyan, et al. Recent advances in diversified recommendation[J]. arXiv preprint, arXiv: 1905.06589, 2019
[10]	Wu Haolun, Zhang Yansen, Ma Chen, et al. A survey of diversification techniques in search and recommendation[J]. arXiv preprint, arXiv: 2212.14464, 2022
[11]	Stirling A. A general framework for analysing diversity in science, technology and society[J]. Journal of the Royal Society Interface, 2007, 4(15): 707−719 doi: 10.1098/rsif.2007.0213
[12]	Niemann K, Wolpers M. A new collaborative filtering approach for increasing the aggregate diversity of recommender systems[C]//Proc of the 19th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2013: 955−963
[13]	Chen Li, Wu Wen, He Liang. Personality and Recommendation Diversity[M]//Emotions and Personality in Personalized Services. Berlin: Springer, 2016
[14]	Adomavicius G, Kwon Y O. Improving aggregate recommendation diversity using ranking-based techniques[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 24(5): 896−911
[15]	Qin Lijing, Zhu Xiaoyan. Promoting diversity in recommendation by entropy regularizer[C]//Proc of the 23rd Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2013: 2698−2704
[16]	AI Box. 一文纵览多样化推荐的现状与发展[EB/OL]. [2023-07-14]. https://zhuanlan.zhihu.com/p/634476644 AI Box. An overview of the current status and development of diversified recommendations[EB/OL]. [2023-07-14]. https://zhuanlan.zhihu.com/p/634476644 (in Chinese)
[17]	Drosou M, Pitoura E. Search result diversification[J]. ACM SIGMOD Record, 2010, 39(1): 41−47 doi: 10.1145/1860702.1860709
[18]	Yu Cong, Lakshmanan L, Amer-Yahia S. It takes variety to make a world: Diversification in recommender systems[C]//Proc of the 12th Int Conf on Extending Database Technology: Advances in Database Technology. New York: ACM, 2009: 368−378
[19]	Boim R, Milo T, Novgorodov S. Diversification and refinement in collaborative filtering recommender[C]//Proc of the 20th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2011: 739−744
[20]	Ziegler C N, McNee S M, Konstan J A, et al. Improving recommendation lists through topic diversification[C]//Proc of the 14th Int Conf on World Wide Web. New York: ACM, 2005: 22−32
[21]	Meymandpour R, Davis J G. Measuring the diversity of recommendations: A preference-aware approach for evaluating and adjusting diversity[J]. Knowledge and Information Systems, 2020, 62(2): 787−811 doi: 10.1007/s10115-019-01371-0
[22]	Sá J, Queiroz Marinho V, Magalhães A R, et al. Diversity vs relevance: A practical multi-objective study in luxury fashion recommendations[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 2405−2409
[23]	Zhang Mi, Hurley N. Avoiding monotony: Improving the diversity of recommendation lists[C]//Proc of the 2008 ACM Conf on Recommender Systems. New York: ACM, 2008: 123−130
[24]	Nagatani K, Sato M. Accurate and diverse recommendation based on users’ tendencies toward temporal item popularity[C]//Proc of the 1st Workshop on Temporal Reasoning in Recommender Systems Co-located with the 11th Int Conf on Recommender Systems (RecSys 2017). New York: ACM, 2017: 35−39
[25]	Onuma K, Tong Hanghang, Faloutsos C. Tangent: A novel, ‘surprise me’, recommendation algorithm[C]//Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2009: 657−666
[26]	Nakatsuji M, Fujiwara Y, Tanaka A, et al. Classical music for rock fans? Novel recommendations for expanding user interests[C]//Proc of the 19th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2010: 949−958
[27]	Oh J, Park S, Yu H, et al. Novel recommendation based on personal popularity tendency[C]//Proc of the 11th IEEE Int Conf on Data Mining. Piscataway, NJ: IEEE, 2011: 507−516
[28]	Sha Chaofeng, Wu Xiaowei, Niu Junyu. A framework for recommending relevant and diverse items[C]//Proc of the 25th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2016: 3868−3874
[29]	Ashkan A, Kveton B, Berkovsky S, et al. Optimal greedy diversity for recommendation[C]//Proc of the 24th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2015: 1742−1748
[30]	He Jingrui, Tong Hanghang, Mei Qiaozhu, et al. Gender: A generic diversified ranking algorithm[C]//Proc of the 26th Annual Conf on Neural Information Processing Systems. Cambridge, MA: MIT, 2012: 1151−1159
[31]	Carbonell J, Goldstein J. The use of MMR, diversity-based reranking for reordering documents and producing summaries[C]//Proc of the 21st Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 1998: 335−336
[32]	Cho E, Myers S A, Leskovec J. Friendship and mobility: User movement in location-based social networks[C]//Proc of the 17th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2011: 1082−1090
[33]	Lu E H C, Lin C Y, Tseng V S. Trip-mine: An efficient trip planning approach with travel time constraints[C]//Proc of the 12th IEEE Int Conf on Mobile Data Management. Piscataway, NJ: IEEE, 2011: 152−161
[34]	Luan Wenjing, Liu Guanjun, Jiang Changjun, et al. MPTR: A maximal-marginal-relevance-based personalized trip recommendation method[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(11): 3461−3474 doi: 10.1109/TITS.2017.2781138
[35]	Kulesza A, Taskar B. Determinantal point processes for machine learning[J]. Foundations and Trends in Machine Learning, 2012, 5(2/3): 123−286
[36]	Chen Laming, Zhang Guoxin, Zhou Eric. Fast greedy map inference for determinantal point process to improve recommendation diversity[C]//Proc of the 32nd Advances in Neural Information Processing Systems. Cambridge, MA: MIT, 2018: 5627−5638
[37]	Gillenwater J, Kulesza A, Mariet Z, et al. A tree-based method for fast repeated sampling of determinantal point processes[C]//Proc of the 36th Int Conf on Machine Learning. New York: PMLR, 2019: 2260−2268
[38]	Gartrell M, Paquet U, Koenigstein N. Low-rank factorization of determinantal point processes[C]//Proc of the 31st AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2017: 1912−1918
[39]	Warlop R, Mary J, Gartrell M. Tensorized determinantal point processes for recommendation[C]//Proc of the 25th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2019: 1605−1615
[40]	Wilhelm M, Ramanathan A, Bonomo A, et al. Practical diversified recommendations on Youtube with determinantal point processes[C]//Proc of the 27th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2018: 2165−2173
[41]	Gan Mingxin, Jiang Rui. Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities[J]. Decision Support Systems, 2013, 55(3): 811−821 doi: 10.1016/j.dss.2013.03.006
[42]	Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proc of the 4th ACM Conf on Recommender Systems. New York: ACM, 2010: 135−142
[43]	Wasilewski J, Hurley N. Incorporating diversity in a learning to rank recommender system[C]//Proc of the 29th Int Florida Artificial Intelligence Research Society Conf. Palo Alto, CA: AAAI, 2016: 572−578
[44]	Gogna A, Majumdar A. DiABlO: Optimization based design for improving diversity in recommender system[J]. Information Sciences, 2017, 378: 59−74 doi: 10.1016/j.ins.2016.10.043
[45]	Shi Yue, Zhao Xiaoxue, Wang Jun, et al. Adaptive diversification of recommendation results via latent factor portfolio[C]//Proc of the 35th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2012: 175−184
[46]	Wu Le, Liu Qi, Chen Enhong, et al. Relevance meets coverage: A unified framework to generate diversified recommendations[J]. ACM Transactions on Intelligent Systems and Technology, 2016, 7(3): 1−30
[47]	Zanitti M, Kosta S, Sørensen J. A user-centric diversity by design recommender system for the movie application domain[C]//Proc of the 27th Web Conf. New York: ACM, 2018: 1381−1389
[48]	Yang Chao, Ai Congcong, Li Renfa. Neighbor diversification-based collaborative filtering for improving recommendation lists[C]//Proc of the 10th IEEE Int Conf on High Performance Computing and Communications & IEEE Int Conf on Embedded and Ubiquitous Computing. Piscataway, NJ: IEEE, 2013: 1658−1664
[49]	Chen H, Karger D R. Less is more: Probabilistic models for retrieving fewer relevant documents[C]//Proc of the 29th Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2006: 429−436
[50]	Xia Long, Xu Jun, Lan Yanyan, et al. Adapting Markov decision process for search result diversification[C]//Proc of the 40th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2017: 535−544
[51]	Cooper C, Lee S H, Radzik T, et al. Random walks in recommender systems: Exact computation and simulations[C]//Proc of the 23rd Int Conf on World Wide Web. New York: ACM, 2014: 811−816
[52]	Nikolakopoulos A N, Karypis G. Recwalk: Nearly uncoupled random walks for top-n recommendation[C]//Proc of the 25th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2019: 150−158
[53]	Christoffel F, Paudel B, Newell C, et al. Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks[C]//Proc of the 9th ACM Conf on Recommender Systems. New York: ACM, 2015: 163−170
[54]	Liu Jianguo, Shi Kerui, Guo Qiang. Solving the accuracy-diversity dilemma via directed random walks[J]. arXiv preprint, arXiv: 1201.6278, 2012
[55]	Paudel B, Bernstein A. Random walks with erasure: Diversifying personalized recommendations on social and information networks[C]//Proc of the 30th Web Conf. New York: ACM, 2021: 2046−2057
[56]	Wang Mengsha, Xiao Yingyuan, Zheng Wenguang, et al. RNDM: A random walk method for music recommendation by considering novelty, diversity, and mainstream[C]//Proc of the 30th Int Conf on Tools with Artificial Intelligence (ICTAI). Piscataway, NJ: IEEE, 2018: 177−183
[57]	Antikacioglu A, Ravi R. Post processing recommender systems for diversity[C]//Proc of the 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2017: 707−716
[58]	Adomavicius G, Kwon Y O. Optimization-based approaches for maximizing aggregate recommendation diversity[J]. INFORMS Journal on Computing, 2014, 26(2): 351−369 doi: 10.1287/ijoc.2013.0570
[59]	Zhai Shuangfei, Chang Kenghao, Zhang Ruofei, et al. Deepintent: Learning attentions for online advertising with recurrent neural networks[C]//Proc of the 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2016: 1295−1304
[60]	Shan Ying, Hoens T R, Jiao Jian, et al. Deep crossing: Web-scale modeling without manually crafted combinatorial features[C]//Proc of the 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2016: 255−262
[61]	Mahajan K C, Palnitkar A, Raul A, et al. CAViaR: Context aware video recommendations[C]//Proc of the 32nd Web Conf. New York: ACM, 2023: 518−522
[62]	He Yifan, Zou Haitao, Yu Hualong, et al. Diversity-aware recommendation by user interest domain coverage maximization[C]//Proc of the 19th Int Conf on Data Mining (ICDM). Piscataway, NJ: IEEE, 2019: 1084−1089
[63]	Lin Zihan, Wang Hui, Mao Jingshu, et al. Feature-aware diversified re-ranking with disentangled representations for relevant recommendation[C]//Proc of the 28th ACM SIGKDD Conf on Knowledge Discovery and Data Mining. New York: ACM, 2022: 3327−3335
[64]	Chen Wanyu, Ren Pengjie, Cai Fei, et al. Improving end-to-end sequential recommendations with intent-aware diversification[C]//Proc of the 29th ACM Int Conf on Information & Knowledge Management. New York: ACM, 2020: 175−184
[65]	Chen Wanyu, Ren Pengjie, Cai Fei, et al. Multi-interest diversification for end-to-end sequential recommendation[J]. ACM Transactions on Information Systems, 2021, 40(1): 1−30
[66]	Choi S, Kim H, Gim M. Do not read the same news! Enhancing diversity and personalization of news recommendation[C]//Proc of the 31st Web Conf. New York: ACM, 2022: 1211−1215
[67]	Wang Jie, Zhou Jinya, Wu Zhen, et al. Toward paper recommendation by jointly exploiting diversity and dynamics in heterogeneous information networks[C]//Proc of the 27th Int Conf on Database Systems for Advanced Applications. Berlin: Springer, 2022: 272−280
[68]	Zhang Mi. Enhancing diversity in top-n recommendation[C]//Proc of the 3rd ACM Conf on Recommender Systems. New York: ACM, 2009: 397−400
[69]	Vargas S, Castells P. Exploiting the diversity of user preferences for recommendation[C]//Proc of the 10th Conf on Open Research Areas in Information Retrieval. New York: ACM, 2013: 129−136
[70]	Di Noia T, Ostuni V C, Rosati J, et al. An analysis of users’ propensity toward diversity in recommendations[C]//Proc of the 8th ACM Conf on Recommender Systems. New York: ACM, 2014: 285−288
[71]	Gogna A, Majumdar A. Balancing accuracy and diversity in recommendations using matrix completion framework[J]. Knowledge-Based Systems, 2017, 125: 83−95 doi: 10.1016/j.knosys.2017.03.023
[72]	Goldstein T, Osher S. The split Bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 323−343 doi: 10.1137/080725891
[73]	Huang Huimin, Shen Hong, Meng Zaiqiao. Item diversified recommendation based on influence diffusion[J]. Information Processing & Management, 2019, 56(3): 939−954
[74]	Lathia N, Hailes S, Capra L, et al. Temporal diversity in recommender systems[C]//Proc of the 33rd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2010: 210−217
[75]	Hao Bin, Zhang Min, Guo Cheng, et al. Diversify or not: Dynamic diversification for personalized recommendation[C]//Proc of the 25th Pacific-Asia Conf on Knowledge Discovery and Data Mining. Berlin: Springer, 2021: 461−472
[76]	Chen Yankai, Yang Yaming, Wang Yujing, et al. Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation[C]//Proc of the 38th IEEE Int Conf on Data Engineering. Piscataway, NJ: IEEE, 2022: 299−311
[77]	Zheng Yu, Gao Chen, Chen Liang, et al. DGCN: Diversified recommendation with graph convolutional networks[C]//Proc of the 30th Web Conf. New York: ACM, 2021: 401−412
[78]	Kang Wang-Cheng, McAuley J. Self-attentive sequential recommendation[C]//Proc of the 18th Int Conf on Data Mining (ICDM). Piscataway, NJ: IEEE, 2018: 197−206
[79]	Lv Fuyu, Jin Taiwei, Yu Changlong, et al. SDM: Sequential deep matching model for online large-scale recommender system[C]//Proc of the 28th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2019: 2635−2643
[80]	Cen Yukuo, Zhang Jianwei, Zou Xu, et al. Controllable multi-interest framework for recommendation[C]//Proc of the 26th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2020: 2942−2951
[81]	Hurley N J. Personalised ranking with diversity[C]//Proc of the 7th ACM Conf on Recommender Systems. New York: ACM, 2013: 379−382
[82]	Takács G, Tikk D. Alternating least squares for personalized ranking[C]//Proc of the 6th ACM Conf on Recommender Systems. New York: ACM, 2012: 83−90
[83]	Li Chao, Liu Zhiyuan, Wu Mengmeng, et al. Multi-interest network with dynamic routing for recommendation at Tmall[C]//Proc of the 28th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2019: 2615−2623
[84]	Lu Yujie, Zhang Shengyu, Huang Yingxuan, et al. Future-aware diverse trends framework for recommendation[C]//Proc of the 30th Web Conf. New York: ACM, 2021: 2992−3001
[85]	Gu Wanrong, Dong Shoubin, Zeng Zhizhao. Increasing recommended effectiveness with Markov chains and purchase intervals[J]. Neural Computing and Applications, 2014, 25(5): 1153−1162 doi: 10.1007/s00521-014-1599-8
[86]	Hu Liang, Cao Longbing, Wang Shoujin, et al. Diversifying personalized recommendation with user-session context[C]//Proc of the 26th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2017: 1858−1864
[87]	Wu Chuhan, Wu Fangzhao, Qi Tao, et al. SentiRec: Sentiment diversity-aware neural news recommendation[C]//Proc of the 1st Conf of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th Int Joint Conf on Natural Language Processing. Stroudsburg, PA: ACL, 2020: 44−53
[88]	Hong Minsung. Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation[J]. Information Sciences, 2021, 562: 259−278 doi: 10.1016/j.ins.2021.02.005
[89]	Lü Linyuan, Zhou Tao. Link prediction in complex networks: A survey[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150−1170 doi: 10.1016/j.physa.2010.11.027
[90]	Sanz-Cruzado J, Castells P. Enhancing structural diversity in social networks by recommending weak ties[C]//Proc of the 12th ACM Conf on Recommender Systems. New York: ACM, 2018: 233−241
[91]	Cheng Peizhe, Wang Shuaiqiang, Ma Jun, et al. Learning to recommend accurate and diverse items[C]//Proc of the 26th Int Conf on World Wide Web. New York: ACM, 2017: 183−192
[92]	Liang Yile, Qian Tieyun, Li Qing, et al. Enhancing domain-level and user-level adaptivity in diversified recommendation[C]//Proc of the 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 747−756
[93]	Huang Yanhua, Wang Weikun, Zhang Lei, et al. Sliding spectrum decomposition for diversified recommendation[C]//Proc of the 27th ACM SIGKDD Conf on Knowledge Discovery & Data Mining. New York: ACM, 2021: 3041−3049
[94]	Yang Ji, Yi Xinyang, Zhiyuan Cheng D, et al. Mixed negative sampling for learning two-tower neural networks in recommendations[C]//Proc of the 29th Web Conf. New York: ACM, 2020: 441−447
[95]	Raza S, Bashir S R, Naseem U. Accuracy meets diversity in a news recommender system[C]//Proc of the 29th Int Conf on Computational Linguistics. New York: ACM, 2022: 3778−3787
[96]	Zhang Xiaoying, Wang Hongning, Li Hang. Disentangled representation for diversified recommendations[C]//Proc of the 16th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2023: 490−498
[97]	Shi Xiaoyu, Liu Quanliang, Xie Hong, et al. Relieving popularity bias in interactive recommendation: A diversity-novelty-aware reinforcement learning approach[J]. ACM Transactions on Information Systems, 2023, 42(2): 1−30
[98]	Chen Zhihong, Wu Jiawei, Li Chenliang, et al. Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 60−69
[99]	Gao Zhaolin, Shen Tianshu, Mai Zheda, et al. Mitigating the filter bubble while maintaining relevance: Targeted diversification with VAE-based recommender systems[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 2524−2531
[100]	Kim B, Wattenberg M, Gilmer J, et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)[C]//Proc of the 35th Int Conf on Machine Learning. New York: PMLR, 2018: 2668−2677
[101]	Wu Qiong, Liu Yong, Miao Chunyan, et al. PD-GAN: Adversarial learning for personalized diversity-promoting recommendation[C]//Proc of the 28th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2019: 3870−3876
[102]	Liu Shuchang, Cai Qingpeng, He Zhankui, et al. Generative flow network for listwise recommendation[J]. arXiv preprint, arXiv: 2306.02239, 2023
[103]	Wang Wenjie, Feng Fuli, Nie Liqiang, et al. User-controllable recommendation against filter bubbles[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 1251−1261
[104]	Wang Wenjie, Feng Fuli, He Xiangnan, et al. Deconfounded recommendation for alleviating bias amplification[C]//Proc of the 27th ACM SIGKDD Conf on Knowledge Discovery & Data Mining. New York: ACM, 2021: 1717−1725
[105]	Gan Lu, Nurbakova D, Laporte L, et al. Enhancing recommendation diversity using determinantal point processes on knowledge graphs[C]//Proc of the 43rd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2020: 2001−2004
[106]	Sun Jianing, Guo Wei, Zhang Dengcheng, et al. A framework for recommending accurate and diverse items using Bayesian graph convolutional neural networks[C]//Proc of the 26th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2020: 2030−2039
[107]	Pal S, Regol F, Coates M. Bayesian graph convolutional neural networks using node copying[J]. arXiv preprint, arXiv: 1911.04965, 2019
[108]	McAuley J, Pandey R, Leskovec J. Inferring networks of substitutable and complementary products[C]//Proc of the 21st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2015: 785−794
[109]	Wang Zihan, Jiang Ziheng, Ren Zhaochun, et al. A path-constrained framework for discriminating substitutable and complementary products in e-commerce[C]//Proc of the 11th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2018: 619−627
[110]	Hao Junheng, Zhao Tong, Li Jin, et al. P-companion: A principled framework for diversified complementary product recommendation[C]//Proc of the 29th ACM Int Conf on Information & Knowledge Management. New York: ACM, 2020: 2517−2524
[111]	Ye Rui, Hou Yuqing, Lei Te, et al. Dynamic graph construction for improving diversity of recommendation[C]//Proc of the 15th ACM Conf on Recommender Systems. New York: ACM, 2021: 651−655
[112]	He Qiang, Zhou Rui, Zhang Xuyun, et al. Keyword search for building service-based systems[J]. IEEE Transactions on Software Engineering, 2016, 43(7): 658−674
[113]	He Qiang, Zhou Rui, Zhang Xuyun, et al. Efficient keyword search for building service-based systems based on dynamic programming[C]//Proc of the 15th Int Conf on Service-Oriented Computing. Berlin: Springer, 2017: 462−470
[114]	Gong Wenwen, Zhang Xuyun, Chen Yifei, et al. DAWAR: Diversity-aware web APIs recommendation for mashup creation based on correlation graph[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 395−404
[115]	Yang Yonghui, Wu Le, Hong Richang, et al. Enhanced graph learning for collaborative filtering via mutual information maximization[C]//Proc of the 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 71−80
[116]	Wu Jiancan, Wang Xiang, Feng Fuli, et al. Self-supervised graph learning for recommendation[C]//Proc of the 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 726−735
[117]	Ma Xiyao, Hu Qian, Gao Zheng, et al. Contrastive co-training for diversified recommendation[C]//Proc of the 20th Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2022: 4422−4430
[118]	Chu Wei, Li Lihong, Reyzin L, et al. Contextual bandits with linear payoff functions[C]//Proc of the 14th Int Conf on Artificial Intelligence and Statistics. New York: PMLR, 2011: 208−214
[119]	Liu Yong, Xiao Yingtai, Wu Qiong, et al. Diversified interactive recommendation with implicit feedback[C]//Proc of the 34th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2020: 4932−4939
[120]	Liang Dawen, Charlin L, McInerney J, et al. Modeling user exposure in recommendation[C]//Proc of the 25th Int Conf on World Wide Web. New York: ACM, 2016: 951−961
[121]	Qin Lijing, Chen Shouyuan, Zhu Xiaoyan. Contextual combinatorial bandit and its application on diversified online recommendation[C]//Proc of the 14th Int Conf on Data Mining. Philadelphia, PA: SIAM, 2014: 461−469
[122]	Ding Qinxu, Liu Yong, Miao Chunyan, et al. A hybrid bandit framework for diversified recommendation[C]//Proc of the 35th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2021: 4036−4044
[123]	Stamenkovic D, Karatzoglou A, Arapakis I, et al. Choosing the best of both worlds: Diverse and novel recommendations through multi-objective reinforcement learning[C]//Proc of the 15th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2022: 957−965
[124]	Zheng Guanjie, Zhang Fuzheng, Zheng Zihan, et al. DRN: A deep reinforcement learning framework for news recommendation[C]//Proc of the 27th World Wide Web Conf. New York: ACM, 2018: 167−176
[125]	Hansen C, Mehrotra R, Hansen C, et al. Shifting consumption towards diverse content on music streaming platforms[C]//Proc of the 14th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2021: 238−246
[126]	Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double q-learning[C]//Proc of the 30th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2016: 2094−2100
[127]	Gao Yixu, Shao Kun, Duan Zhijian, et al. Efficient dual-process cognitive recommender balancing accuracy and diversity[C]//Proc of the 27th Int Conf on Database Systems for Advanced Applications. Berlin: Springer, 2022: 389−400
[128]	GroupLens Research. MovieLens[EB/OL]. [2023-07-14]. https://movielens.org/
[129]	豆瓣. 豆瓣[EB/OL]. [2024-07-25]. https://www.douban.com/ Douban. Douban[EB/OL]. [2024-07-25]. https://www.douban.com/ (in Chinese)
[130]	Netflix. Netflix[EB/OL]. [2024-07-25]. https://www.netflix.com
[131]	McAuley J. Amazon Dataset[EB/OL]. [2024-07-25]. https://jmcauley.ucsd.edu/data/amazon/
[132]	MyAnimeList. MyAnimeList[EB/OL]. [2024-07-25]. https://myanimelist.net/
[133]	Yelp. Yelp Dataset[EB/OL]. [2024-07-25]. https://www.yelp.com/
[134]	阿里云天池. 天猫数据集[EB/OL]. [2024-07-25]. https://tianchi.aliyun.com/dataset/dataDetail?dataId=53 Aliyun Tianchi. Tmail Dataset[EB/OL]. [2024-07-25]. https://tianchi.aliyun.com/dataset/dataDetail?dataId=53 (in Chinese)
[135]	Last. fm. Last. fm[EB/OL]. [2024-07-25]. https://www.last.fm/
[136]	MSNews. MSNews[EB/OL]. [2024-07-25]. https://msnews.github.io/
[137]	Tamber. Steam video games dataset[EB/OL]. [2024-07-25]. https://www.kaggle.com/datasets/tamber/steam-video-games
[138]	Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5−53 doi: 10.1145/963770.963772
[139]	Herlocker J L, Konstan J A, Borchers A, et al. An algorithmic framework for performing collaborative filtering[C]//Proc of the 22nd Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 1999: 230−237
[140]	Middleton S E, Shadbolt N R, De Roure D C. Ontological user profiling in recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 54−88 doi: 10.1145/963770.963773
[141]	Zhang Yuancao, Séaghdha D Ó, Quercia D, et al. Auralist: Introducing serendipity into music recommendation[C]//Proc of the 5th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2012: 13−22
[142]	Li Jinming, Zhang Wentao, Wang Tian, et al. GPT4Rec: A generative framework for personalized recommendation and user interests interpretation[J]. arXiv preprint, arXiv: 2304.03879, 2023