Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects
This tackles the problem of group discovery for users in social media, but appears incremental as it builds on existing recommender systems by incorporating social interactions.
The paper addresses the need for effective group recommendations in social media platforms like Last.fm, where users form groups to share interests, by proposing a multi-output recommender that leverages mutual effects among items, groups, and friends to improve recommendations.
Due to the development of social media technology, it becomes easier for users to gather together to form groups. Take the Last.fm for example, users can join groups they may be interested where they can share their loved songs and discuss topics about songs and singers. However, the number of groups grows over time, users need effective groups recommendations in order to meet more like-minded users.