Hierarchy Visualization for Group Recommender Systems

Preference Elicitation for Group Recommender Systems

In group decision making, users’ behaviour are influenced by their long-term and group-induced preferences. However, how to leverage them is challenging due to their dynamic nature, which is also dependent on the specific group settings. In our work, we employ a group recommendation model that utilizes both types of preferences and we analyze alternative ways of combing them, under diverse grou...

Hierarchical graph maps for visualization of collaborative recommender systems

Group Recommender Systems: Combining Individual Models

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple cr...

Ratings Estimation on Group Recommender Systems

Recommender systems have been developed to nd personalized content for users. The personalization techniques used in these systems, usually focus on satisfying the needs of individual users. Nevertheless, within some domains there is a need to generate recommendations to groups of users instead of individuals. In order to detect the interest of a group of users, several aggregation techniques h...

User Issues in Social Group Recommender Systems

Social group recommender systems aim to recommend items of interest to a social group or a community of people. The user issues in such systems cannot be addressed by examining the satisfaction of their members as individuals. Rather, group satisfaction should be studied as a result of the interaction and interface methods that support group dynamics and interaction. This report first introduce...