A New Evolutionary Approach to Recommender Systems

作者：Kim Hyun Tae; An Jinung; Ahn Chang Wook^*

来源：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2014, E97D(3): 622-625.

DOI：10.1587/transinf.E97.D.622

• recommender systems
• user's preference
• interactive evolutionary computation
• content-based filtering
• data grouping

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items; the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

• 出版日期2014-3