Improving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data

The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of the recommender systems. These systems assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy system capabilities in determining the borders of user interests, it seems reasonable to combine it with social network information and the factor of time. In this work, for the first time, we try to assess the efficiency of the recommender systems by combining fuzzy logic, longitudinal data, and social network information such as tags, friendship, and membership in groups. Also the impact of the proposed algorithm for improving the accuracy of the recommender systems is studied by specifying the neighborhood and the border between the users’ preferences over time. The results obtained reveal that using longitudinal data in social network information in memory-based recommender systems improves the accuracy of these systems.