A Probabilistic Graph-Based Method to Improve Recommender System Accuracy

Abstract

The last two decades have seen a surge of data on the Web which causes overwhelming users with huge amount of information. Recommender systems (RSs) help users to efficiently find desirable items among a pool of items. RSs often rely on collaborating filtering (CF), where history of transactions are analyzed in order to recommend items. High accuracy, and low time and implementation complexity are most important factors for evaluating the performance algorithms which current methods have the shortage of all or some of them. In this paper, a probabilistic graph-based recommender system (PGB) is proposed based on graph theory and Markov chain with improved accuracy and low complexity. In the proposed method, selecting each item for recommendation is conditioned by considering recommended items in the previous steps. This approach uses a probabilistic model to consider the items which are likely to be preferred by users in the future. Experimental results performed on two real-world datasets including Movielens and Jester, demonstrate that the proposed method significantly outperforms several traditional and state-of-the-art recommender systems.