skip to main content
research-article

Diversifying Sequential Recommendation with Retrospective and Prospective Transformers

Published: 29 April 2024 Publication History

Abstract

Previous studies on sequential recommendation (SR) have predominantly concentrated on optimizing recommendation accuracy. However, there remains a significant gap in enhancing recommendation diversity, particularly for short interaction sequences. The limited availability of interaction information in short sequences hampers the recommender’s ability to comprehensively model users’ intents, consequently affecting both the diversity and accuracy of recommendation. In light of the above challenge, we propose reTrospective and pRospective Transformers for dIversified sEquential Recommendation (TRIER). The TRIER addresses the issue of insufficient information in short interaction sequences by first retrospectively learning to predict users’ potential historical interactions, thereby introducing additional information and expanding short interaction sequences, and then capturing users’ potential intents from multiple augmented sequences. Finally, the TRIER learns to generate diverse recommendation lists by covering as many potential intents as possible.
To evaluate the effectiveness of TRIER, we conduct extensive experiments on three benchmark datasets. The experimental results demonstrate that TRIER significantly outperforms state-of-the-art methods, exhibiting diversity improvement of up to 11.36% in terms of intra-list distance (ILD@5) on the Steam dataset, 3.43% ILD@5 on the Yelp dataset and 3.77% in terms of category coverage (CC@5) on the Beauty dataset. As for accuracy, on the Yelp dataset, we observe notable improvement of 7.62% and 8.63% in HR@5 and NDCG@5, respectively. Moreover, we found that TRIER reveals more significant accuracy and diversity improvement for short interaction sequences.

References

[1]
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, and Zheng Wen. 2015. Optimal greedy diversity for recommendation. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015, Qiang Yang and Michael J. Wooldridge (Eds.). AAAI Press, 1742–1748. http://ijcai.org/Abstract/15/248
[2]
Chems Eddine Berbague, Nour El Islem Karabadji, Hassina Seridi, Panagiotis Symeonidis, Yannis Manolopoulos, and Wajdi Dhifli. 2021. An overlapping clustering approach for precision, diversity and novelty-aware recommendations. Expert Syst. Appl. 177 (2021), 114917.
[3]
Jaime G. Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In SIGIR’98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 24–28, 1998, Melbourne, Australia. ACM, 335–336.
[4]
Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020. Controllable multi-interest framework for recommendation. In KDD’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23–27, 2020. ACM, 2942–2951.
[5]
Laming Chen, Guoxin Zhang, and Eric Zhou. 2018. Fast greedy MAP inference for determinantal point process to improve recommendation diversity. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3–8, 2018, Montréal, Canada. 5627–5638. https://proceedings.neurips.cc/paper/2018/hash/dbbf603ff0e99629dda5d75b6f75f966-Abstract.html
[6]
Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2019. Joint neural collaborative filtering for recommender systems. ACM Trans. Inf. Syst. 37, 4 (2019), 39:1–39:30.
[7]
Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten de Rijke. 2020. Improving end-to-end sequential recommendations with intent-aware diversification. In CIKM’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19–23, 2020. ACM, 175–184.
[8]
Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten de Rijke. 2022. Multi-interest diversification for end-to-end sequential recommendation. ACM Trans. Inf. Syst. 40, 1 (2022), 20:1–20:30.
[9]
Xu Chen, Zhenlei Wang, Hongteng Xu, Jingsen Zhang, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Data augmented sequential recommendation based on counterfactual thinking. IEEE Trans. Knowl. Data Eng. 35, 9 (2023), 9181–9194.
[10]
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5–9, 2018. ACM, 108–116.
[11]
Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, and Gabriele Tolomei. 2023. The dark side of explanations: Poisoning recommender systems with counterfactual examples. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23–27, 2023. ACM, 2426–2430.
[12]
Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3–7, 2017. ACM, 183–192.
[13]
Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu. 2023. TiCoSeRec: Augmenting data to uniform sequences by time intervals for effective recommendation. IEEE Transactions on Knowledge and Data Engineering (2023).
[14]
Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu. 2023. Uniform sequence better: Time interval aware data augmentation for sequential recommendation. In Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7–14, 2023. AAAI Press, 4225–4232.
[15]
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13–17, 2019. ACM, 417–426.
[16]
Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Trans. Inf. Syst. 39, 1 (2020), 10:1–10:42.
[17]
Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, and Yong Li. 2022. Causal inference in recommender systems: A survey and future directions. CoRR abs/2208.12397 (2022). arXiv:2208.12397
[18]
Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, and Benjamin Taskar. 2014. Expectation-maximization for learning determinantal point processes. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8–13 2014, Montreal, Quebec, Canada. 3149–3157. https://proceedings.neurips.cc/paper/2014/hash/4462bf0ddbe0d0da40e1e828ebebeb11-Abstract.html
[19]
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, and Sergei Vassilvitskii. 2019. A tree-based method for fast repeated sampling of determinantal point processes. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA(Proceedings of Machine Learning Research, Vol. 97). PMLR, 2260–2268. http://proceedings.mlr.press/v97/gillenwater19a.html
[20]
Ruining He and Julian J. McAuley. 2016. Fusing similarity models with Markov chains for sparse sequential recommendation. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. IEEE Computer Society, 191–200.
[21]
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30, 12 (2018), 2354–2366.
[22]
Yang He, Xu Zheng, Rui Xu, and Ling Tian. 2023. Knowledge-based recommendation with contrastive learning. High-Confidence Computing 3, 4 (2023), 100151.
[23]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings. http://arxiv.org/abs/1511.06939
[24]
Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08–12, 2018. ACM, 505–514.
[25]
Marius Kaminskas and Derek Bridge. 2017. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7, 1 (2017), 2:1–2:42.
[26]
Wang-Cheng Kang and Julian J. McAuley. 2018. Self-attentive sequential recommendation. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17–20, 2018. IEEE Computer Society, 197–206.
[27]
Naime Ranjbar Kermany, Jian Yang, Jia Wu, and Luiz Pizzato. 2022. Fair-SRS: A fair session-based recommendation system. In WSDM’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21–25, 2022. ACM, 1601–1604.
[28]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings. http://arxiv.org/abs/1412.6980
[29]
Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In KDD’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23–27, 2020. ACM, 1748–1757.
[30]
Matevz Kunaver and Tomaz Pozrl. 2017. Diversity in recommender systems - A survey. Knowl. Based Syst. 123 (2017), 154–162.
[31]
Jingjing Li, Ke Lu, Zi Huang, and Heng Tao Shen. 2021. On both cold-start and long-tail recommendation with social data. IEEE Trans. Knowl. Data Eng. 33, 1 (2021), 194–208.
[32]
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06–10, 2017. ACM, 1419–1428.
[33]
Shuang Li, Yuezhi Zhou, Di Zhang, Yaoxue Zhang, and Xiang Lan. 2017. Learning to diversify recommendations based on matrix factorization. In 15th IEEE Intl. Conf. on Dependable, Autonomic and Secure Computing, 15th Intl. Conf. on Pervasive Intelligence and Computing, 3rd Intl. Conf. on Big Data Intelligence and Computing and Cyber Science and Technology Congress, DASC/PiCom/DataCom/CyberSciTech 2017, Orlando, FL, USA, November 6–10, 2017. IEEE Computer Society, 68–74.
[34]
Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, and Ji-Rong Wen. 2022. Feature-aware diversified re-ranking with disentangled representations for relevant recommendation. In KDD’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14–18, 2022. ACM, 3327–3335.
[35]
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19–23, 2018. ACM, 1831–1839.
[36]
Yuli Liu, Christian J. Walder, and Lexing Xie. 2022. Determinantal point process likelihoods for sequential recommendation. In SIGIR’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, 1653–1663.
[37]
Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, and Haihong Tang. 2020. Diversified interactive recommendation with implicit feedback. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7–12, 2020. AAAI Press, 4932–4939. https://ojs.aaai.org/index.php/AAAI/article/view/5931
[38]
Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S. Yu. 2021. Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer. In SIGIR’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 1608–1612.
[39]
Yujie Lu, Shengyu Zhang, Yingxuan Huang, Luyao Wang, Xinyao Yu, Zhou Zhao, and Fei Wu. 2021. Future-aware diverse trends framework for recommendation. In WWW’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19–23, 2021. ACM / IW3C2, 2992–3001.
[40]
Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, and Maarten de Rijke. 2023. Improving transformer-based sequential recommenders through preference editing. ACM Trans. Inf. Syst. 41, 3 (2023), 71:1–71:24.
[41]
Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Peiyu Liu, Jun Ma, and Maarten de Rijke. 2022. Mixed information flow for cross-domain sequential recommendations. ACM Trans. Knowl. Discov. Data 16, 4 (2022), 64:1–64:32.
[42]
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9–13, 2015, Ricardo Baeza-Yates, Mounia Lalmas, Alistair Moffat, and Berthier A. Ribeiro-Neto (Eds.). ACM, 43–52.
[43]
Zhiqiang Pan, Fei Cai, Wanyu Chen, Chonghao Chen, and Honghui Chen. 2022. Collaborative graph learning for session-based recommendation. ACM Trans. Inf. Syst. 40, 4 (2022), 72:1–72:26.
[44]
Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, and Maarten de Rijke. 2020. Star graph neural networks for session-based recommendation. In CIKM’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19–23, 2020. 1195–1204.
[45]
Apurva Pathak, Kshitiz Gupta, and Julian McAuley. 2017. Generating and personalizing bundle recommendations on steam. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1073–1076.
[46]
Lijing Qin and Xiaoyan Zhu. 2013. Promoting diversity in recommendation by entropy regularizer. In IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3–9, 2013, Francesca Rossi (Ed.). IJCAI/AAAI, 2698–2704. http://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/6511
[47]
Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022. Contrastive learning for representation degeneration problem in sequential recommendation. In WSDM’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21–25, 2022. ACM, 813–823.
[48]
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017. ACM, 130–137.
[49]
Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. RepeatNet: A repeat aware neural recommendation machine for session-based recommendation. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 – February 1, 2019. AAAI Press, 4806–4813.
[50]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26–30, 2010, Michael Rappa, Paul Jones, Juliana Freire, and Soumen Chakrabarti (Eds.). ACM, 811–820.
[51]
Chaofeng Sha, Xiaowei Wu, and Junyu Niu. 2016. A framework for recommending relevant and diverse items. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016. IJCAI/AAAI Press, 3868–3874. http://www.ijcai.org/Abstract/16/544
[52]
Sumit Sidana, Charlotte Laclau, and Massih-Reza Amini. 2018. Learning to recommend diverse items over implicit feedback on PANDOR. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2–7, 2018. ACM, 427–431.
[53]
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (2014), 1929–1958.
[54]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3–7, 2019. ACM, 1441–1450.
[55]
Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2023. Parallel split-join networks for shared account cross-domain sequential recommendations. IEEE Trans. Knowl. Data Eng. 35, 4 (2023), 4106–4123.
[56]
Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5–9, 2018. ACM, 565–573.
[57]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA. 5998–6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
[58]
Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21–25, 2019. ACM, 345–354.
[59]
Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10–16, 2019. ijcai.org, 3771–3777.
[60]
Wenjie Wang, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2022. User-controllable recommendation against filter bubbles. In SIGIR’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, 1251–1261.
[61]
Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Counterfactual data-augmented sequential recommendation. In SIGIR’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11–15, 2021. ACM, 347–356.
[62]
Romain Warlop, Jérémie Mary, and Mike Gartrell. 2019. Tensorized determinantal point processes for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4–8, 2019. ACM, 1605–1615.
[63]
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H. Chi, and Jennifer Gillenwater. 2018. Practical diversified recommendations on YouTube with determinantal point processes. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22–26, 2018. ACM, 2165–2173.
[64]
Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Fernando Diaz, and Xue Liu. 2022. A survey of diversification techniques in search and recommendation. CoRR abs/2212.14464 (2022).
[65]
Qiong Wu, Yong Liu, Chunyan Miao, Yin Zhao, Lu Guan, and Haihong Tang. 2019. Recent advances in diversified recommendation. CoRR abs/1905.06589 (2019). http://arxiv.org/abs/1905.06589
[66]
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9–12, 2022. IEEE, 1259–1273.
[67]
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S. Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019. Recurrent convolutional neural network for sequential recommendation. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13–17, 2019. ACM, 3398–3404.
[68]
Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, and Taiqing Wang. 2023. DGRec: Graph neural network for recommendation with diversified embedding generation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023–3 March 2023. ACM, 661–669.
[69]
Qing Yin, Hui Fang, Zhu Sun, and Yew-Soon Ong. 2023. Understanding diversity in session-based recommendation. ACM Trans. Inf. Syst. (2023).
[70]
Mi Zhang and Neil Hurley. 2008. Avoiding monotony: Improving the diversity of recommendation lists. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, October 23–25, 2008. ACM, 123–130.
[71]
Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu C. Aggarwal, and Tyler Derr. 2023. Fairness and diversity in recommender systems: A survey. CoRR abs/2307.04644 (2023).
[72]
Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, and Yong Li. 2021. DGCN: Diversified recommendation with graph convolutional networks. In WWW’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19–23, 2021. ACM / IW3C2, 401–412.
[73]
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19–23, 2020. ACM, 1893–1902.
[74]
Tao Zhou, Ri-Qi Su, Run-Ran Liu, Luo-Luo Jiang, Bing-Hong Wang, and Yi-Cheng Zhang. 2009. Accurate and diverse recommendations via eliminating redundant correlations. New Journal of Physics 11, 12 (2009), 123008.
[75]
Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee. 2021. Fairness among new items in cold start recommender systems. In SIGIR’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11–15, 2021. ACM, 767–776.
[76]
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web, WWW 2005, Chiba, Japan, May 10–14, 2005. ACM, 22–32.
[77]
Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. 2001. Using temporal data for making recommendations. In UAI’01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2–5, 2001. Morgan Kaufmann, 580–588. https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=146&proceeding_id=17

Index Terms

  1. Diversifying Sequential Recommendation with Retrospective and Prospective Transformers

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 42, Issue 5
    September 2024
    809 pages
    EISSN:1558-2868
    DOI:10.1145/3618083
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 April 2024
    Online AM: 17 March 2024
    Accepted: 11 March 2024
    Revised: 28 December 2023
    Received: 15 August 2023
    Published in TOIS Volume 42, Issue 5

    Check for updates

    Author Tags

    1. Retrospective transformer
    2. prospective transformer
    3. diverse
    4. sequential recommendation

    Qualifiers

    • Research-article

    Funding Sources

    • National Key R&D Program of China
    • Natural Science Foundation of China
    • Natural Science Foundation of Shandong Province

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 829
      Total Downloads
    • Downloads (Last 12 months)829
    • Downloads (Last 6 weeks)50
    Reflects downloads up to 28 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media