Group recommendation

TLDR

Recommendation systems are widely used for individual users, but extending them to groups—where members may have divergent tastes—is an unresolved challenge relevant to scenarios such as shared movies, travel, and dining. The study aims to formalize group recommendation by analyzing its desiderata and proposing a semantics that balances relevance and member disagreements. The authors design and implement efficient algorithms that compute group recommendations based on the proposed formal semantics. User studies on Amazon Mechanical Turk show that accounting for disagreements significantly improves recommendation effectiveness, and experiments on MovieLens demonstrate the algorithms scale to 10 million ratings.

Abstract

We study the problem of group recommendation. Recommendation is an important information exploration paradigm that retrieves interesting items for users based on their profiles and past activities. Single user recommendation has received significant attention in the past due to its extensive use in Amazon and Netflix. How to recommend to a group of users who may or may not share similar tastes, however, is still an open problem. The need for group recommendation arises in many scenarios: a movie for friends to watch together, a travel destination for a family to spend a holiday break, and a good restaurant for colleagues to have a working lunch. Intuitively, items that are ideal for recommendation to a group may be quite different from those for individual members. In this paper, we analyze the desiderata of group recommendation and propose a formal semantics that accounts for both item relevance to a group and disagreements among group members. We design and implement algorithms for efficiently computing group recommendations. We evaluate our group recommendation method through a comprehensive user study conducted on Amazon Mechanical Turk and demonstrate that incorporating disagreements is critical to the effectiveness of group recommendation. We further evaluate the efficiency and scalability of our algorithms on the MovieLens data set with 10M ratings.