Improving Collaborative Filtering Recommendation
Pang-Ming Chu, Hong-Ru Tsai, Shie-Jue Lee, Shing‐Tai Pan · 2018
Collaborative filtering recommender systems traditionally recommend products to users solely based on the user-item rating matrix and are simple, convenient to use.In this paper, we focus on two main issues, data sparsity and scalability.Data sparsity can lead to inaccurate recommendations, while scalability may cause an unacceptably long delay before valuable recommendations are acquired.We propose a novel approach to deal with these two issues.Word2Vec is employed to build item vectors from the user comments.Through the user-item rating matrix, user vectors of all the users are then obtained.A clustering technique is applied to reduce the time complexity related to the large numbers of items and users.Experimental results of real data sets are shown to demonstrate the effectiveness of our proposed approach.