A Framework for Recommender System Based on Game Theory in Social Networks
Lü Yang, Tao Hong, Anilkmar Kothalil Gopalakrishnan · 2018
This paper presents a recommender system based on a game theory in which the recommendations are made from user-item ratings. The user-item ratings are the most essential factor for a social network to maintain its social relationships among users. It is not possible for a social network to force all of its users to rate items and such techniques are not formed yet. In this paper, game theory and SimRank (Similarity Based on Random Walk) are used as a core algorithm to build the recommender system. The user-item ratings dataset is decomposed into similar groups based on the user ratings by the game theory. The similarities among the 'similar interest' users are calculated with the SimRank algorithm. Based on the user similarity information, user profile and rating dataset, the presented system would provide proper recommendation of items to its users. The goal of the presented system is to identify how the user- item ratings can affect in user friendship relations to make a correct recommendation and the carried out experimental analysis used to evaluate the accuracy of the system.