Generating Consensus Explanations for Group Recommendations

Shabnam Najafian, Nava Tintarev
2018 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization - UMAP '18  
In some scenarios, like music, people often consume items in groups. However, reaching a consensus is difficult, and often compromises need to be made. Such compromises can potentially help users expand their tastes. They can also lead to outright rejection of the recommended items. One way to avoid this is to explain recommendations that are surprising, or even expected to be disliked, by an individual user. This paper presents an approach for generating explanations for groups. We propose
more » ... rithms for selecting a sequence of songs for a group to consume. These algorithms consider consensus but have different trade-offs. Next, using these algorithms we generated explanations in a layered evaluation using synthetic data. We studied the influence of these explanations in structured interviews with users (n=16) on user satisfaction.
doi:10.1145/3213586.3225231 dblp:conf/um/NajafianT18 fatcat:mvyuqymrqvfnlgnw4hqzwdnxka