Design of a recommender system based on users’ behavior and collaborative location and tracking

• Recommender combining collaborative, content-based, and context-aware techniques.
• Automatic ratings considering the users’ movements after receiving recommendations.
• Minimize sparsity and cold-start drawbacks and provide most valuable recommendations.
• Avoid relying on users to rate recommendations.
• Experiments confirm the effectiveness and the efficiency of this proposal.

During the last years, mobile devices allow incorporating users’ location and movements into recommendations to potentially suggest most valuable information. In this context, this paper presents a hybrid recommender algorithm that combines users’ location and preferences and the content of the items located close to such users. This algorithm also includes a way of providing implicit ratings considering the users’ movements after receiving recommendations, aimed at measuring the users’ interest for the recommended items. Conducted experiments measure the effectiveness and the efficiency of our recommender algorithm, as well as the impact of implicit ratings.