Conceptual framework for recommendation system based on distributed user ratings

A recommender system is an automated collaborative filtering system that helps users to decide what they are interested in by extracting their own preferences. Most studies related to recommendation system have focused on centralized environment causing several serious problems such as fraud rating and privacy issues. In this paper, however, we propose the distributed recommender system with FOAF on P2P environment. This system can recommend users without the centralized server, which keeps updating their profiles. In order to find out the most eligible users to be recommended, user grouping (selecting objects) is one of the most important processes in the whole recommendation. Thereby, we have exploited cosine-based similarity method to cluster the users in the same level. More importantly, RFR (Recommend-Feedback-Re-recommend) algorithm is suggested to improve the confidence of recommendation. For the experiment, we have used the MovieLens datasets and have tested the performance by using "F1-measure" and Mean Absolute Error (MAE). As a conclusion, we have improved the robustness of the system. Also, we have shown the possibility of distributed recommender system on semantic web.