Group Recommender Systems (GRSys) are tools that help groups of people find items of interest and/or make decisions together [1]. GRSys employ algorithms to provide personalized recommendations that satisfy the preferences of all group members as much as possible. These systems apply to various domains and applications, including social media [2], online shopping [3], collaborative work environments [4], etc.
With research on single-users recommender systems (RecSys) growing over time [5], advancements in the area, i.e., approaches such as collaborative filtering [6], constraint-based [7] and rule-based [8] methods, neural networks [9], and hybrid approaches [10] have been largely transferable to GRSys. However, specializing in groups often requires ad hoc approaches as their complex dynamics do not simply map to individuals' needs and behaviors. Questions such as
"How to deal with different (or even opposing) preferences in the group?"
"How to combine individual preferences?"
"Should the ratings of all group members have the same importance in different contextual situations?"
are prevalent among GRSys and yet remain open-ended and hardly generalizable to different domains. More generally, ecological problems such as the explainability, adaptation, privacy, fairness, and evaluation methodology of the GRSys still present opportunities for research and development. We highlight how these challenges apply to GRSys and how the proposed workshop encourages discussion and collaboration for multidisciplinary research on these and other issues.
Rahul Katarya. 2017. A systematic review of group recommender systems techniques. In 2017 International conference on intelligent sustainable systems (ICISS). IEEE, 425–428.
Sanjay Purushotham and C-C Jay Kuo. 2016. Personalized group recommender systems for location-and event-based social networks. ACM Transactions on Spatial Algorithms and Systems (TSAS) 2, 4 (2016), 1–29.
Linyuan Lu, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou. 2012. Recommender systems. Physics reports 519, 1 (2012), 1–49.
David Contreras, Maria Salamó, and Ludovico Boratto. 2021. Integrating collaboration and leadership in conversational group recommender systems. ACM Transactions on Information Systems (TOIS) 39, 4 (2021), 1–32.
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-based systems 46 (2013), 109–132.
Abinash Pujahari and Dilip Singh Sisodia. 2021. Preference relation based collaborative filtering with graph aggregation for group recommender system. Applied Intelligence 51, 2 (2021), 658–672.
Alexander Felfernig, Nava Tintarev, TNT Trang, and Martin Stettinger. 2021. Designing explanations for group recommender systems. arXiv preprint arXiv:2102.12413 (2021).
Vineet Padmanabhan, Siva Krishna Seemala, and Wilson Naik Bhukya. 2011. A rule based approach to group recommender systems. In International Workshop on Multi-disciplinary Trends in Artificial Intelligence. Springer, 26–37.
Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018. Attentive Group Recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, 645–654. https://doi.org/10.1145/3209978.3209998
Sriharsha Dara, C Ravindranath Chowdary, and Chintoo Kumar. 2020. A survey on group recommender systems. Journal of Intelligent Information Systems 54, 2 (2020), 271–295.