ABSTRACT
Group recommender systems (GRS) focus on recommending items to groups of users. GRS need to tackle the heterogeneity of group members’ preferences and produce recommendations of high overall utility while also considering some sense of fairness among group members. This work plans to aim for novel applications of GRS involving construction of large-scale groups of users and focusing on the long-term fairness of these groups which is in contrast with current research that concentrates on small groups of ephemeral nature. We believe that these directions could bring results of significant societal impact and scope of the effect expanding beyond currently considered GRS domains, e.g., helping to mitigate the filter bubble problem
Supplemental Material
- [1] [n.d.]. https://www.kaggle.com/datasets/gpreda/covid19-tweets.Google Scholar
- L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. 2002. Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups. In Hypermedia: Openness, Structural Awareness, and Adaptivity, Siegfried Reich, Manolis M. Tzagarakis, and Paul M. E. De Bra (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 280–295.Google Scholar
- Chenwei Cai, Ruining He, and Julian McAuley. 2017. SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation. In Proceedings of the 26th International Joint Conference on Artificial Intelligence(Melbourne, Australia) (IJCAI’17). AAAI Press, 1476–1482.Google ScholarCross Ref
- Berardina De Carolis, Stefano Ferilli, and Nicola Orio. 2014. Recommending Music to Groups in Fitness Classes. In Music Technology meets Philosophy - From Digital Echos to Virtual Ethos: Joint Proceedings of the 40th International Computer Music Conference, ICMC 2014, and the 11th Sound and Music Computing Conference, SMC 2014, Athens, Greece, September 14-20, 2014. Michigan Publishing. https://hdl.handle.net/2027/spo.bbp2372.2014.267Google Scholar
- Sriharsha Dara, C. Ravindranath Chowdary, and Chintoo Kumar. 2020. A survey on group recommender systems. J. Intell. Inf. Syst. 54, 2 (2020), 271–295. https://doi.org/10.1007/s10844-018-0542-3Google ScholarCross Ref
- Hai Ha Do, PWC Prasad, Angelika Maag, and Abeer Alsadoon. 2019. Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review. Expert Systems with Applications 118 (2019), 272–299. https://doi.org/10.1016/j.eswa.2018.10.003Google ScholarDigital Library
- Patrik Dokoupil and Ladislav Peska. To be published in 2022. Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization(Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3511047.3537650Google ScholarDigital Library
- Mesut Kaya, Derek Bridge, and Nava Tintarev. 2020. Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. In Fourteenth ACM Conference on Recommender Systems(Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 101–110. https://doi.org/10.1145/3383313.3412232Google ScholarDigital Library
- Ladislav Malecek and Ladislav Peska. 2021. Fairness-Preserving Group Recommendations With User Weighting. Association for Computing Machinery, New York, NY, USA, 4–9. https://doi.org/10.1145/3450614.3461679Google ScholarDigital Library
- Judith Masthoff. 2011. Group Recommender Systems: Combining Individual Models. 677–702. https://doi.org/10.1007/978-0-387-85820-3_21Google ScholarCross Ref
- Judith Masthoff and Amra Delić. 2022. Group Recommender Systems: Beyond Preference Aggregation. Springer US, New York, NY, 381–420. https://doi.org/10.1007/978-1-0716-2197-4_10Google ScholarCross Ref
- Judith Masthoff and Albert Gatt. 2006. In pursuit of satisfaction and the prevention of embarrassment: Affective state in group recommender systems. User Model. User-Adapt. Interact. 16 (09 2006), 281–319. https://doi.org/10.1007/s11257-006-9008-3Google ScholarDigital Library
- Joseph F. McCarthy and Theodore D. Anagnost. 2000. MUSICFX: an arbiter of group preferences for computer supported collaborative workouts. In CSCW 2000, Proceeding on the ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, PA, USA, December 2-6, 2000, Wendy A. Kelloggand Steve Whittaker (Eds.). ACM, 348. https://doi.org/10.1145/358916.361976Google ScholarDigital Library
- Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW ’14). Association for Computing Machinery, New York, NY, USA, 677–686. https://doi.org/10.1145/2566486.2568012Google ScholarDigital Library
- Mark O’Connor, Dan Cosley, Joseph Konstan, and John Riedl. 2001. PolyLens: A recommender system for groups of user.199–218.Google Scholar
- Maria Soledad Pera and Yiu-Kai Ng. 2013. A group recommender for movies based on content similarity and popularity. Inf. Process. Manag. 49, 3 (2013), 673–687. https://doi.org/10.1016/j.ipm.2012.07.007Google ScholarDigital Library
- Ladislav Peska and Patrik Dokoupil. To be published in 2022. Towards Results-level Proportionality for Multi-objective Recommender Systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3477495.3531787Google ScholarDigital Library
- Ladislav Peska and Ladislav Malecek. 2021. Coupled or Decoupled Evaluation for Group Recommendation Methods?. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021 co-located with the 15th ACM Conference on Recommender Systems (RecSys 2021), Amsterdam, The Netherlands, September 25, 2021(CEUR Workshop Proceedings, Vol. 2955), Eva Zangerle, Christine Bauer, and Alan Said (Eds.). CEUR-WS.org. http://ceur-ws.org/Vol-2955/paper1.pdfGoogle Scholar
- Shaina Raza and Chen Ding. 2022. News recommender system: a review of recent progress, challenges, and opportunities. Artif. Intell. Rev. 55, 1 (2022), 749–800. https://doi.org/10.1007/s10462-021-10043-xGoogle ScholarDigital Library
- Dimitris Sacharidis. 2019. Top-N Group Recommendations with Fairness. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (Limassol, Cyprus) (SAC ’19). Association for Computing Machinery, New York, NY, USA, 1663–1670. https://doi.org/10.1145/3297280.3297442Google ScholarDigital Library
- Lara Quijano Sánchez, Juan A. Recio-García, Belén Díaz-Agudo, and Guillermo Jiménez-Díaz. 2011. Happy Movie: A Group Recommender Application in Facebook. In Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, May 18-20, 2011, Palm Beach, Florida, USA, R. Charles Murray and Philip M. McCarthy (Eds.). AAAI Press. http://aaai.org/ocs/index.php/FLAIRS/FLAIRS11/paper/view/2556Google Scholar
- Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, and Panayiotis Tsaparas. 2017. Fairness in Package-to-Group Recommendations. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 371–379. https://doi.org/10.1145/3038912.3052612Google ScholarDigital Library
- Maria Stratigi, Jyrki Nummenmaa, Evaggelia Pitoura, and Kostas Stefanidis. 2020. Fair Sequential Group Recommendations. Association for Computing Machinery, New York, NY, USA, 1443–1452. https://doi.org/10.1145/3341105.3375766Google ScholarDigital Library
- Antonela Tommasel, Juan Manuel Rodriguez, and Daniela Godoy. 2021. I Want to Break Free! Recommending Friends from Outside the Echo Chamber. Association for Computing Machinery, New York, NY, USA, 23–33. https://doi.org/10.1145/3460231.3474270Google ScholarDigital Library
- Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 3597–3606. https://doi.org/10.18653/v1/2020.acl-main.331Google ScholarCross Ref
- Qiliang Zhu and Lei Wang. 2020. Context-Aware Restaurant Recommendation for Group of People. In 2020 IEEE World Congress on Services, SERVICES 2020, Beijing, China, October 18-23, 2020. IEEE, 51–54. https://doi.org/10.1109/SERVICES48979.2020.00025Google ScholarCross Ref
Index Terms
- Long-term fairness for Group Recommender Systems with Large Groups
Recommendations
Fairness-preserving Group Recommendations With User Weighting
UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationGroup recommendations are an extension of ”single-user” personalized recommender systems (RS), where the final recommendations should comply with preferences of several group members. An important challenge in group RS is the problem of fairness, i.e., ...
Member contribution-based group recommender system
Developing group recommender systems (GRSs) is a vital requirement in many online service systems to provide recommendations in contexts in which a group of users are involved. Unfortunately, GRSs cannot be effectively supported using traditional ...
Single User Group Recommendations
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and PersonalizationGoing to restaurants is also a social activity; people often go to restaurants with family, friends, or colleagues. However, most restaurant finder systems, such as TripAdvisor, allow users to search for restaurants matching only one user’s ...
Comments