TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
The adaptive web
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In this era of overabundant information and content, people increasingly rely on recommender systems to identify those information items that best meet their needs and interests. Movie recommender systems, like the one used by Netflix, attempt to predict which films a given person will enjoy watching. While these systems help single individuals making decisions, they provide limited support for groups of people. This work explores how to create recommender systems for groups that can combine multiple user profiles and predict which movies a group of users will collectively enjoy the most. We built a prototype using Netflix REST API based on the results of a formative study of the watching habits of 60 actual Netflix users and examined their views of how a group recommendation system would fit in with their current habits. We conducted a preliminary evaluation with a focus group which validated our approach to group recommendations, revealing that this type of system could facilitate social interactions by sparking discussion about movies, directors, and actors among viewers. This prototype provides a valuable platform for further exploring group decision making in this context.