A Coupled Clustering Approach for Items Recommendation

Recommender systems are very useful due to the huge volume of information available on the Web. It helps users alleviate the information overload problem by recommending users with the personalized information, products or services (called items). Collaborative filtering and content-based recommendation algorithms have been widely deployed in e-commerce web sites. However, they both suffer from the scalability problem. In addition, there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items. In this paper, we propose a hybrid recommendation algorithm by combing the content-based and collaborative filtering techniques as well as incorporating the coupled similarity. Our method firstly partitions items into several item groups by using a coupled version of k-modes clustering algorithm, where the similarity between items is measured by the Coupled Object Similarity considering coupling between items. The collaborative filtering technique is then used to produce the recommendations for active users. Experimental results show that our proposed hybrid recommendation algorithm effectively solves the scalability issue of recommender systems and provides a comparable recommendation quality when lacking most of the item features.