A novel multi-objective evolutionary algorithm for recommendation systems

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. A new topic diversity indicator is introduced, which can be used to measure various kinds of items in a recommendation list.A new probabilistic multi-objective evolutionary algorithm (PMOEA) is presented, which is suitable for the recommendation systems.A new crossover operator is proposed to generate new solution, called the multi-parent probability genetic operator.The experimental results show that PMOEA can achieve a good balance between precision and diversity.

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