skip to main content
10.1145/3583780.3614801acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article
Open access

Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation

Published: 21 October 2023 Publication History

Abstract

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.

References

[1]
Himan Abdollahpouri. 2019. Popularity Bias in Ranking and Recommendation. In AIES. 529--530.
[2]
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In RecSys. 42--46.
[3]
Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided Exposure Bias in Recommendation. CoRR, Vol. abs/2006.15772 (2020). showeprint[arXiv]2006.15772
[4]
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).
[5]
Mohammad Al-Rubaie and J Morris Chang. 2019. Privacy-preserving machine learning: Threats and solutions. IEEE Security & Privacy, Vol. 17, 2 (2019), 49--58.
[6]
Pegah Malekpour Alamdari, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Ali Asghar Safaei, and Aso Darwesh. 2020. A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access, Vol. 8 (2020), 115694--115716.
[7]
Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019. Local popularity and time in top-n recommendation. In ECIR. 861--868.
[8]
Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, and Bibek Paudel. 2020. Adversarial learning for debiasing knowledge graph embeddings. arXiv preprint arXiv:2006.16309 (2020).
[9]
Oren Barkan, Noam Koenigstein, Eylon Yogev, and Ori Katz. 2019. CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations. In RecSys. 228--236.
[10]
Pedro G. Campos, Fernando D'iez, and Manuel Sánchez-Monta nés. 2011. Towards a More Realistic Evaluation: Testing the Ability to Predict Future Tastes of Matrix Factorization-Based Recommenders. In RecSys. 309--312.
[11]
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. TOIS, Vol. 41, 3 (2023), 1--39.
[12]
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential Recommendation with User Memory Networks. In WSDM. 108--116.
[13]
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In WWW. 2172--2182.
[14]
Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020. ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance. In SIGIR. 579--588.
[15]
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. In RecSys.
[16]
Aminu Da'u and Naomie Salim. 2020. Recommendation system based on deep learning methods: a systematic review and new directions. In Artificial Intelligence Review. 2709----2748.
[17]
Carlo De Medio, Carla Limongelli, Filippo Sciarrone, and Marco Temperini. 2020. MoodleREC: A recommendation system for creating courses using the moodle e-learning platform. Computers in Human Behavior, Vol. 104 (2020), 106168.
[18]
Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In RecSys. 152--160.
[19]
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 2020. How to Learn Item Representation for Cold-Start Multimedia Recommendation?. In MM. 3469--3477.
[20]
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022. Sequential recommendation via stochastic self-attention. In WWW. 2036--2047.
[21]
Robert H. Frank. 2020. In Praise of Herd Mentality. The Atlantic (MArch 2020).
[22]
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020. Hierarchical User Profiling for E-Commerce Recommender Systems. In WSDM. 223--231.
[23]
Sukhmeen Kaur Hanjraw, Kuldeep Yadav, and Karamjit Kaur. 2019. Web Personalization Recommendation System Through Semantics. In ICCS. 658--661.
[24]
Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016. Vista: A visually, socially, and temporally-aware model for artistic recommendation. In RecSys. 309--316.
[25]
Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based recommendation. In RecSys. 161--169.
[26]
Ruining He and Julian McAuley. 2016a. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM. 191--200.
[27]
Ruining He and Julian McAuley. 2016b. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507--517.
[28]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In WWW. 173--182.
[29]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
[30]
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys. 241--248.
[31]
Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. In SIGIR. 505--514.
[32]
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2020. A re-visit of the popularity baseline in recommender systems. In SIGIR. 1749--1752.
[33]
Juyong Jiang, Jae Boum Kim, Yingtao Luo, Kai Zhang, and Sunghun Kim. 2022. AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation. arXiv preprint arXiv:2205.08776 (2022).
[34]
Tatsuya Kameda and Reid Hastie. 2015. Herd Behavior. 1--14.
[35]
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014. Correcting Popularity Bias by Enhancing Recommendation Neutrality. RecSys Posters, Vol. 805 (2014).
[36]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197--206.
[37]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.
[38]
Frank Klinker. 2011. Exponential moving average versus moving exponential average. Mathematische Semesterberichte, Vol. 58 (2011), 97--107.
[39]
Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram. 2018. An Adversarial Approach to Improve Long-Tail Performance in Neural Collaborative Filtering. In CIKM. 1491--1494.
[40]
Abdurhman Kurdi. 2021. The Effects of Herd Mentality on Behavior. Ph.,D. Dissertation.
[41]
Dong-Joo Lee, Jae-Hyeon Ahn, and Youngsok Bang. 2011. Managing Consumer Privacy Concerns in Personalization: A Strategic Analysis of Privacy Protection. In MIS Quarterly. 423--444.
[42]
Hui Li, Ye Liu, Nikos Mamoulis, and David S Rosenblum. 2019. Translation-based sequential recommendation for complex users on sparse data. TKDE, Vol. 32, 8 (2019), 1639--1651.
[43]
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419--1428.
[44]
Miaomiao Li and Licheng Wang. 2019. A Survey on Personalized News Recommendation Technology. IEEE Access, Vol. 7 (2019), 145861--145879.
[45]
Wenxuan Li, Yinghong Ma, and Lixin Zhao. 2022. Data-driven Individual Influence Analysis: A Case Study of Chinese Film Industry. In IEIT. 173--177.
[46]
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: short-term attention/memory priority model for session-based recommendation. In SIGKDD. 1831--1839.
[47]
Mary Loxton, Robert Truskett, Brigitte Scarf, Laura Sindone, George Baldry, and Yinong Zhao. 2020. Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour. Journal of Risk and Financial Management, Vol. 13, 8 (2020).
[48]
Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical gating networks for sequential recommendation. In SIGKDD. 825--833.
[49]
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020. Exploring data splitting strategies for the evaluation of recommendation models. In RecSys. 681--686.
[50]
Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In ICDM. 497--506.
[51]
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press.
[52]
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In RecSys. 130--137.
[53]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811--820.
[54]
Ha?im Sak, Andrew Senior, and Françoise Beaufays. 2014. Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. arXiv:1402.1128 [cs, stat].
[55]
Camilo Salazar, Jose Aguilar, Julián Monsalve-Pulido, and Edwin Montoya. 2021. Affective recommender systems in the educational field. A systematic literature review. Computer Science Review, Vol. 40 (2021), 100377.
[56]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW. 285--295.
[57]
Martin Saveski and Amin Mantrach. 2014. Item Cold-Start Recommendations: Learning Local Collective Embeddings. In RecSys. 89--96.
[58]
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 CIKM. 1441--1450.
[59]
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. 2021. Sparse-Interest Network for Sequential Recommendation. In WSDM. 598--606.
[60]
Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565--573.
[61]
Catherine E. Tucker. 2014. Social Networks, Personalized Advertising, and Privacy Controls. Journal of Marketing Research, Vol. 51, 5 (2014), 546--562.
[62]
Jun Wang, Arjen P De Vries, and Marcel JT Reinders. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR. 501--508.
[63]
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021. Deconfounded recommendation for alleviating bias amplification. In SIGKDD. 1717--1725.
[64]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In SIGIR. 165--174.
[65]
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In SIGKDD. 1791--1800.
[66]
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2023. A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation. TKDE, Vol. 35, 5 (2023), 4425--4445.
[67]
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In AAAI, Vol. 33. 346--353.
[68]
Lianghao Xia, Chao Huang, and Chuxu Zhang. 2022. Self-supervised hypergraph transformer for recommender systems. In SIGKDD. 2100--2109.
[69]
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. Graph Contextualized Self-Attention Network for Session-based Recommendation. In IJCAI, Vol. 19. 3940--3946.
[70]
Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye, and Yewang Chen. 2022. Neutralizing Popularity Bias in Recommendation Models. In SIGIR. 2623--2628.
[71]
Mengyue Yang, Guohao Cai, Furui Liu, Jiarui Jin, Zhenhua Dong, Xiuqiang He, Jianye Hao, Weiqi Shao, Jun Wang, and Xu Chen. 2023. Debiased recommendation with user feature balancing. TOIS, Vol. 41, 4 (2023), 1--25.
[72]
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In RecSys. 269--277.
[73]
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In SIGIR. 729--732.
[74]
Jian Feng Zhang. 2013. The Application of Color Psychological Effect on Fashion Design. In Silk, Protective Clothing and Eco-Textiles, Vol. 796. 474--478.
[75]
Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, and Liang Wang. 2020. Personalized graph neural networks with attention mechanism for session-aware recommendation. TKDE, Vol. 34, 8 (2020), 3946--3957.
[76]
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In SIGIR. 11--20.
[77]
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu. 2022. Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation. TKDE (2022).
[78]
Lei Zheng, Ziwei Fan, Chun-Ta Lu, Jiawei Zhang, and Philip S Yu. 2019. Gated spectral units: Modeling co-evolving patterns for sequential recommendation. In SIGIR. 1077--1080.
[79]
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021a. Disentangling user interest and conformity for recommendation with causal embedding. In WWW. 2980--2991.
[80]
Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu. 2021b. Cold-start Sequential Recommendation via Meta Learner. AAAI, Vol. 35, 5 (May 2021), 4706--4713.
[81]
Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, and Chunyan Miao. 2023. Selfcf: A simple framework for self-supervised collaborative filtering. TORS, Vol. 1, 2 (2023), 1--25.
[82]
Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM. In IJCAI, Vol. 17. 3602--3608.
[83]
Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021. Popularity-Opportunity Bias in Collaborative Filtering. In WSDM. 85--93.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2023

Check for updates

Author Tags

  1. non-personalized recommender
  2. popularity trends
  3. recommender system

Qualifiers

  • Research-article

Conference

CIKM '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 531
    Total Downloads
  • Downloads (Last 12 months)455
  • Downloads (Last 6 weeks)37
Reflects downloads up to 21 Dec 2024

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media