skip to main content
10.1145/3640457.3691713acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
extended-abstract

Leveraging Monte Carlo Tree Search for Group Recommendation

Published: 08 October 2024 Publication History

Abstract

Group recommenders aim to provide recommendations that satisfy the collective preferences of multiple users, a challenging task due to the diverse individual tastes and conflicting interests to be balanced. This is often accomplished by using aggregation techniques that select items on which the group can agree. Traditional aggregators struggle with these complexities, as items are chosen independently, leading to sub-optimal recommendations lacking diversity, novelty, or fairness. In this paper, we propose an aggregation technique that leverages Monte Carlo Tree Search (MCTS) to enhance group recommendations. MCTS is used to explore and evaluate candidate recommendation sequences to optimize overall group satisfaction. We also investigate the integration of MCTS with LLMs aiming at better understanding interactions between user preferences and recommendation sequences to inform the search. Experimental evaluations, although preliminary, showed that our proposal outperforms existing aggregation techniques in terms of relevance and beyond-accuracy aspects of recommendations. The LLM integration achieved positive results for recommendations’ relevance. Overall, this work highlights the potential of heuristic search techniques to tackle the complexities of group recommendations.

Supplemental Material

MP4 File
Short presentation video

References

[1]
Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems. 119–126.
[2]
Rocío Cañamares, Pablo Castells, and Alistair Moffat. 2020. Offline evaluation options for recommender systems. Information Retrieval Journal 23, 4 (2020), 387–410.
[3]
Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 335–336.
[4]
Rémi Coulom. 2006. Efficient selectivity and backup operators in Monte-Carlo tree search. In International conference on computers and games. Springer, 72–83.
[5]
Alexander Felfernig, Müslüm Atas, Denis Helic, Thi Ngoc Trang Tran, Martin Stettinger, and Ralph Samer. 2023. Algorithms for group recommendation. In Group recommender systems: An introduction. Springer, 29–61.
[6]
Bruce Ferwerda, Mark P. Graus, Andreu Vall, Marko Tkalcic, and Markus Schedl. 2017. How Item Discovery Enabled by Diversity Leads to Increased Recommendation List Attractiveness. In Proceedings of the Symposium on Applied Computing (Marrakech, Morocco) (SAC ’17). Association for Computing Machinery, New York, NY, USA, 1693–1696. https://doi.org/10.1145/3019612.3019899
[7]
Alois Gruson, Praveen Chandar, Christophe Charbuillet, James McInerney, Samantha Hansen, Damien Tardieu, and Ben Carterette. 2019. Offline evaluation to make decisions about playlistrecommendation algorithms. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 420–428.
[8]
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024. Large language models are zero-shot rankers for recommender systems. In European Conference on Information Retrieval. Springer, 364–381.
[9]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263–272.
[10]
Mesut Kaya, Derek Bridge, and Nava Tintarev. 2020. Ensuring fairness in group recommendations by rank-sensitive balancing of relevance. In Proceedings of the 14th ACM Conference on recommender systems. 101–110.
[11]
Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, and Peter Stone. 2017. Designing better playlists with monte carlo tree search. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31. 4715–4720.
[12]
Ladislav Malecek and Ladislav Peska. 2021. Fairness-preserving group recommendations with user weighting. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 4–9.
[13]
Judith Masthoff. 2010. Group recommender systems: Combining individual models. In Recommender systems handbook. Springer, 677–702.
[14]
Madison Grace McClung and Angèle Christin. 2018. Filter Bubbles And Music Streaming : The Influence of Personalization And Recommendation Algorithms on Music Discovery Via Streaming Platforms.
[15]
Allen Nie, Ching-An Cheng, Andrey Kolobov, and Adith Swaminathan. 2023. Importance of Directional Feedback for LLM-based Optimizers. In NeurIPS 2023 Foundation Models for Decision Making Workshop. https://www.microsoft.com/en-us/research/publication/importance-of-directional-feedback-for-llm-based-optimizers/
[16]
Ladislav Peska and Ladislav Malecek. 2021. Coupled or Decoupled Evaluation for Group Recommendation Methods?. In Perspectives@ RecSys.
[17]
Dilina Chandika Rajapakse and Douglas Leith. 2022. Fast and accurate user cold-start learning using monte carlo tree search. In Proceedings of the 16th ACM Conference on Recommender Systems. 350–359.
[18]
Dimitris Sacharidis. 2019. Diversity and Novelty in Social-Based Collaborative Filtering. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (Larnaca, Cyprus) (UMAP ’19). ACM, New York, NY, USA, 139–143. https://doi.org/10.1145/3320435.3320479
[19]
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501–509.
[20]
Javier Sanz-Cruzado and Pablo Castells. 2018. Enhancing structural diversity in social networks by recommending weak ties. In Proceedings of the 12th ACM conference on recommender systems. 233–241.
[21]
Javier Sanz-Cruzado and Pablo Castells. 2018. Enhancing Structural Diversity in Social Networks by Recommending Weak Ties. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). ACM, New York, NY, USA, 233–241. https://doi.org/10.1145/3240323.3240371
[22]
Maria Taramigkou, Efthimios Bothos, Konstantinos Christidis, Dimitris Apostolou, and Gregoris Mentzas. 2013. Escape the bubble: Guided exploration of music preferences for serendipity and novelty. In Proceedings of the 7th ACM conference on Recommender systems. 335–338.
[23]
Christoph Trattner, Alan Said, Ludovico Boratto, and Alexander Felfernig. 2023. Evaluating group recommender systems. In Group recommender systems: an introduction. Springer, 63–75.
[24]
Robbie CM van Aert. 2023. Meta-analyzing partial correlation coefficients using Fisher’s z transformation. Research Synthesis Methods 14, 5 (2023), 768–773.
[25]
David A Walker. 2003. JMASM9: converting Kendall’s tau for correlational or meta-analytic analyses. Journal of Modern Applied Statistical Methods 2 (2003), 525–530.
[26]
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. 2017. Fairness-aware group recommendation with pareto-efficiency. In Proceedings of the eleventh ACM conference on recommender systems. 107–115.
[27]
Quan Yuan, Gao Cong, and Chin-Yew Lin. 2014. COM: a generative model for group recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 163–172.
[28]
Zirui Zhao, Wee Sun Lee, and David Hsu. 2024. Large language models as commonsense knowledge for large-scale task planning. Advances in Neural Information Processing Systems 36 (2024).

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2024

Check for updates

Author Tags

  1. Large Language Models
  2. diversity
  3. fairness
  4. group recommendation
  5. heuristic search
  6. novelty
  7. recommender systems

Qualifiers

  • Extended-abstract
  • Research
  • Refereed limited

Funding Sources

  • Consejo Nacional de Investigaciones Científicas y Técnicas

Conference

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 153
    Total Downloads
  • Downloads (Last 12 months)153
  • Downloads (Last 6 weeks)18
Reflects downloads up to 18 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media