An improved recommendation algorithm based on Bhattacharyya Coefficient
Collaborative Filtering (CF) has become one of the most successful approaches for providing personalized product recommendations to users. Neighborhood based CF is one of the main forms among all CFs, which is widely used in commercial domain. However, neighborhood based CF suffers from new user cold start problem in sparse rating data. In this paper, we propose an improved neighborhood based CF recommendation algorithm based on Bhattacharyya Coefficient to address the new user cold start problem. The proposed algorithm combines the item neighborhood information with the user neighborhood information to improve the recommendation precision. Finally, the proposed algorithm is tested on a real dataset and the results show the proposed algorithm has the better recommendation performance.
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)