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 recom- mendation model that utilizes both types of preferences and we analyze alternative ways of combing them, under diverse group settings. Based on a custom-designed simulation process, we examine the effect of these combinations on the model performance. The experimental results demon- strate that a combination scheme weighing more the long-Term preferences is well adapted to the scenarios where the group setting has no impact on users' preferences, but when users tend to be cooperative or when their preferences diverge in the context of groups, users seem to benefit more from a recommender that quicker adapts to the group-induced preferences, which reflect their newly emerging interests.