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Trending Now: Modeling Trend Recommendations

Authors:

Branislav Kveton,

Venkataramana Kini,

Hao WangAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 294 - 305

https://doi.org/10.1145/3604915.3608810

Published: 14 September 2023 Publication History

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Abstract

Modern recommender systems usually include separate recommendation carousels such as ‘trending now’ to list trending items and further boost their popularity, thereby attracting active users. Though widely useful, such ‘trending now’ carousels typically generate item lists based on simple heuristics, e.g., the number of interactions within a time interval, and therefore still leave much room for improvement. This paper aims to systematically study this under-explored but important problem from the new perspective of time series forecasting. We first provide a set of rigorous definitions related to item trendiness and formulate the trend recommendation task as a one-step time series forecasting problem. We then propose a deep latent variable model, dubbed Trend Recommender (TrendRec), to forecast items’ future trends and generate trending item lists. Furthermore, we design associated evaluation protocols for trend recommendation. Experiments on real-world datasets from various domains show that our TrendRec significantly outperforms the baselines, verifying our model’s effectiveness.

References

[1]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 42–46.

Digital Library

[2]

Luca Maria Aiello, Georgios Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Göker, Ioannis Kompatsiaris, and Alejandro Jaimes. 2013. Sensing trending topics in Twitter. IEEE Transactions on multimedia 15, 6 (2013), 1268–1282.

Digital Library

[3]

Ziad Al-Halah, Rainer Stiefelhagen, and Kristen Grauman. 2017. Fashion forward: Forecasting visual style in fashion. In Proceedings of the IEEE international conference on computer vision. 388–397.

[4]

Sarah A Alkhodair, Steven HH Ding, Benjamin CM Fung, and Junqiang Liu. 2020. Detecting breaking news rumors of emerging topics in social media. Information Processing & Management 57, 2 (2020), 102018.

Digital Library

[5]

Ting Bai, Lixin Zou, Wayne Xin Zhao, Pan Du, Weidong Liu, Jian-Yun Nie, and Ji-Rong Wen. 2019. CTrec: A long-short demands evolution model for continuous-time recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 675–684.

Digital Library

[6]

James Bennett, Stan Lanning, 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol. 2007. New York, 35.

[7]

Rahul Bhagat, Srevatsan Muralidharan, Alex Lobzhanidze, and Shankar Vishwanath. 2018. Buy it again: Modeling repeat purchase recommendations. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 62–70.

Digital Library

[8]

George EP Box and Gwilym M Jenkins. 1968. Some recent advances in forecasting and control. Journal of the Royal Statistical Society. Series C (Applied Statistics) 17, 2 (1968), 91–109.

[9]

George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons.

Digital Library

[10]

Yuri M Brovman. 2019. Complementary Item Recommendations at eBay Scale.

[11]

Renqin Cai, Xueying Bai, Zhenrui Wang, Yuling Shi, Parikshit Sondhi, and Hongning Wang. 2018. Modeling sequential online interactive behaviors with temporal point process. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 873–882.

Digital Library

[12]

Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang. 2021. Zero-shot recommender systems. arXiv preprint arXiv:2105.08318 (2021).

[13]

Ziwei Fan, Hao Ding, Anoop Deoras, and Trong Nghia Hoang. 2023. Personalized federated domain adaptation for item-to-item recommendation. In Proceedings of the UAI 2023. https://www.amazon.science/publications/personalized-federated-domain-adaptation-for-item-to-item-recommendation

[14]

Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S Yu. 2021. Continuous-time sequential recommendation with temporal graph collaborative transformer. In Proceedings of the 30th ACM international conference on information & knowledge management. 433–442.

Digital Library

[15]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[16]

Rob Hyndman, Anne B Koehler, J Keith Ord, and Ralph D Snyder. 2008. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.

[17]

Tim Januschowski, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, and Laurent Callot. 2018. Deep Learning for Forecasting: Current Trends and Challenges.Foresight: The International Journal of Applied Forecasting51 (2018).

[18]

Kelvin Kan, Franccois-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, and Jan Gasthaus. 2022. Multivariate quantile function forecaster. In International Conference on Artificial Intelligence and Statistics. PMLR, 10603–10621.

[19]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206.

[20]

Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems 32 (2019).

[21]

Yunzhe Li, Yue Ding, Bo Chen, Xin Xin, Yule Wang, Yuxiang Shi, Ruiming Tang, and Dong Wang. 2021. Extracting Attentive Social Temporal Excitation for Sequential Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 998–1007.

Digital Library

[22]

Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister. 2021. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting 37, 4 (2021), 1748–1764.

[23]

Yujie Lu, Shengyu Zhang, Yingxuan Huang, Luyao Wang, Xinyao Yu, Zhou Zhao, and Fei Wu. 2021. Future-aware diverse trends framework for recommendation. In Proceedings of the Web Conference 2021. 2992–3001.

Digital Library

[24]

Yifei Ma, Balakrishnan Narayanaswamy, Haibin Lin, and Hao Ding. 2020. Temporal-contextual recommendation in real-time. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2291–2299.

Digital Library

[25]

Utkarsh Mall, Kevin Matzen, Bharath Hariharan, Noah Snavely, and Kavita Bala. 2019. Geostyle: Discovering fashion trends and events. In Proceedings of the IEEE/CVF international conference on computer vision. 411–420.

[26]

Michael Mathioudakis and Nick Koudas. 2010. Twittermonitor: trend detection over the twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. 1155–1158.

Digital Library

[27]

Mor Naaman, Hila Becker, and Luis Gravano. 2011. Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for Information Science and Technology 62, 5 (2011), 902–918.

Digital Library

[28]

Youngsuk Park, Danielle Maddix, Franccois-Xavier Aubet, Kelvin Kan, Jan Gasthaus, and Yuyang Wang. 2022. Learning quantile functions without quantile crossing for distribution-free time series forecasting. In International Conference on Artificial Intelligence and Statistics. PMLR, 8127–8150.

[29]

Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. Advances in neural information processing systems 31 (2018).

[30]

David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus. 2019. High-dimensional multivariate forecasting with low-rank gaussian copula processes. Advances in neural information processing systems 32 (2019).

[31]

David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting 36, 3 (2020), 1181–1191.

[32]

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. 1441–1450.

Digital Library

[33]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 1235–1244.

Digital Library

[34]

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 Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1791–1800.

Digital Library

[35]

Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka. 2017. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053 (2017).

[36]

Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, 2020. Mind: A large-scale dataset for news recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3597–3606.

[37]

An Yan, Chaosheng Dong, Yan Gao, Jinmiao Fu, Tong Zhao, Yi Sun, and Julian McAuley. 2022. Personalized complementary product recommendation. In Companion Proceedings of the Web Conference 2022. 146–151.

Digital Library

[38]

Louis Yu, Sitaram Asur, and Bernardo A Huberman. 2011. What trends in Chinese social media. arXiv preprint arXiv:1107.3522 (2011).

[39]

Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C Maddix, and Yuyang Wang. 2022. First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. arXiv preprint arXiv:2212.08151 (2022).

[40]

Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021. Language models as recommender systems: Evaluations and limitations. (2021).

[41]

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 Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 11–20.

Digital Library

[42]

Zhe Zhao, Paul Resnick, and Qiaozhu Mei. 2015. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th international conference on world wide web. 1395–1405.

Digital Library

[43]

Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference 2021. 2980–2991.

Digital Library

[44]

Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1079–1088.

Digital Library

[45]

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.

Cited By

Lin ZDing HHoang NKveton BDeoras AWang HAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Pre-trained Recommender Systems: A Causal Debiasing PerspectiveProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635779(424-433)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635779

Index Terms

Trending Now: Modeling Trend Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Cited By

Lin ZDing HHoang NKveton BDeoras AWang HAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Pre-trained Recommender Systems: A Causal Debiasing PerspectiveProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635779(424-433)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635779

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