A User Interest Recommendation Based on Collaborative Filtering

Wenqi Wu, Jianfang Wang, Randong Liu, Zhenpeng Gu, Yongli Liu
2016 Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)   unpublished
The traditional collaborative filtering algorithm cannot response user interest with time and is lack of time effectiveness. These problems lead to poor recommendation quality. On the basis of the neighbor-based collaborative filtering, a fused method of improved similarity and user interest is proposed. To begin with, we compute similarity from global perspectives obtained with Jaccard similarity, local perspectives obtained with Bhattacharyya Coefficient. Furthermore, we adopt the forgetting
more » ... urve to represent the user interest preference, adding the interest weight to the new similarity method to update user interest. Finally, we make recommendation prediction by calculating similarity using the method. Experimental results on the Movielens datasets demonstrate that our approach has advantages over state-of-the-art methods in terms of both the discovery of user interest preference and providing highly accuracy recommendations.
doi:10.2991/aiie-16.2016.122 fatcat:jcs2fjijsjhylfwltrwxrwp4sq