A Clustering-Based Similarity Measurement for Collaborative Filtering

Similarity measurement is a crucial process in collaborative filtering. User similarity is computed solely based on the numerical ratings of users. In this paper, we argue that the social information of users should be also taken into consideration to improve the performance of traditional similarity measurements. To achieve this, we propose a clustering-based similarity measurement approach incorporating user social information. In order to cluster the users effectively, we propose a novel distance metric based on taxonomy tree which can easily process the numerical and categorical information of users. Meanwhile, we also address how to determine the contribution of different types of information in the distance metric. After clustering the users, we introduce the incorporating strategy of our proposed similarity measurement. We perform a series of experiments on a real world dataset and compare the performance of our approach against that of traditional approaches. Experiments demonstrate that the proposed approach considerably outperforms the traditional approaches.