[WIP] This repository contains recent research papers, datasets, and source codes (if any) for Group Recommendation. Free free to create a PR to merge.

1. CoFeel: Using Emotions for Social Interaction in Group Recommender Systems. RecSys 2012. [pdf]
2. Mathematical Modeling and Analysis of Product Rating with Partial Information. TKDD 2010. [pdf]
3. Enhancing Group Recommendation by Incorporating Social Relationship Interactions. GROUP 2010. [pdf]
4. Preference Aggregation in Group Recommender Systems for Committee Decision-Making. RecSys 2009. [pdf]
5. A group recommendation system with consideration of interactions among group members. Expert Sys. Appl. 2008. [pdf]
6. TV Program Recommendation for Multiple Viewers Based on user Profile Merging. UMUAI 2006. [pdf]
7. Adaptive Radio: Achieving Consensus Using Negative Preferences. GROUP 2005. [pdf]
8. More Than the Sum of Its Members: Challenges for Group Recommender Systems. AVI 2004. [pdf]
9. MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. CSCW 1998. [pdf]

1. ART: group recommendation approaches for automatically detected groups. IJMLC 2015. [pdf]
2. Group Recommendations with Rank Aggregation and Collaborative Filtering. RecSys 2010. [pdf]
3. Group-Based Recipe Recommendations: Analysis of Data Aggregation Strategies. RecSys 2010. [pdf]
4. State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups. Inform. Retrieval and Mining in Distrib. Environments 2010. [pdf]
5. Group Recommendation: Semantics and Efficiency. VLDB 2009. [pdf]
6. Recommendation to Groups. The Adaptive Web 2007. [pdf]
7. Group Modeling in a Public Space: Methods, Techniques, Experiences. WSEAS 2005. [pdf]
8. Flytrap: Intelligent Group Music Recommendation. IUI 2002. [pdf]
9. PolyLens: A Recommender System for Groups of Users. ECSCW 2001. [pdf]

1. Centrality-based Group Formation in Group Recommender Systems. WWW 2017. [pdf]
2. Recommending New Items to Ephemeral Groups Using Contextual User Influence. RecSys 2016. [pdf]
3. A General Graph-based Model for Recommendation in Event-based Social Networks. ICDE 2015. [pdf]
   Extend: A General Recommendation Model for Heterogeneous Networks. TKDE 2016. [pdf]
4. COM: a Generative Model for Group Recommendation. KDD 2014. [pdf]
5. Development of a group recommender application in a Social Network. KBS 2014 [pdf]
6. Combining Latent Factor Model with Location Features for Event-based Group Recommendation. KDD 2013. [pdf]
7. Users' Satisfaction in Recommendation Systems for Groups: an Approach Based on Noncooperative Games. WWW 2013. [pdf]
8. Probabilistic Group Recommendation via Information Matching. WWW 2013. [pdf]
9. Exploring Personal Impact for Group Recommendation. CIKM 2012. [pdf]
10. Exploring Social Influence for Recommendation - A Generative Model Approach. SIGIR 2012. [pdf]
11. Event-based Social Networks: Linking the Online and Offline Social Worlds. KDD 2012. [pdf]
12. Group Recommendation using Feature Space Representing Behavioral Tendency and Power Balance among Members. RecSys 2011. [pdf]
13. CATS: A Synchronous Approach to Collaborative Group Recommendation. FLAIRS 2006. [pdf]

1. Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation. SIGIR 2020 [pdf]
2. GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation. SIGIR 2020 [pdf]
3. GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation. SIGIR 2020 [pdf]
4. Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation. SIGIR 2019. [pdf]
5. Attentive Group Recommendation. SIGIR 2018. [pdf], [code]
   Extend: Social-enhanced Attentive Group Recommendation. TKDE 2019. [pdf], [code]
6. Deep Modeling of Group Preferences for Group-Based Recommendation. AAAI 2014. [pdf]

1. Meetup [paper], [data]
2. MovieLens [paper]
3. Mafengwo [paper], [data]
4. CAMRa2011 [paper], [data]
5. Plancast [paper]
6. Whrrl [paper]
7. Jiepang [paper]

_{Note: you need to contact the authors for the original crawled datasets or follow the experimental setup section in the papers to reconstruct your own datasets.}