A novel multi-objective evolutionary algorithm for recommendation systems

Abstract

Nowadays, the recommendation algorithm has been used in lots of information systems and Internet applications. The recommendation algorithm can pick out the information that users are interested in. However, most traditional recommendation algorithms only consider the precision as the evaluation metric of the performance. Actually, the metrics of diversity and novelty are also very important for recommendation. Unfortunately, there is a conflict between precision and diversity in most cases. To balance these two metrics, some multi-objective evolutionary algorithms are applied to the recommendation algorithm. In this paper, we firstly put forward a kind of topic diversity metric. Then, we propose a novel multi-objective evolutionary algorithm for recommendation systems, called PMOEA. In PMOEA, we present a new probabilistic genetic operator. Through the extensive experiments, the results demonstrate that the combination of PMOEA and the recommendation algorithm can achieve a good balance between precision and diversity.