A Novel Multidimensional Comments and Co-preference Aware Recommendation Algorithm

Abstract

A recommendation system creates a personalized experience for each customer, which helps companies boost the average order value and the amount of revenue generated from each customer. In a typical recommendation system, comments typify the group wisdom of users, which can reflect their feelings toward the product in multiple dimensions. Co-preference mirrors common preference of a group of users. By mining the multidimensional comments and co-preference relationship comprehensively, it is justifiable to recommend products that both have a good reputation and conform to users’ interests. However, the existing related methods have two problems. Firstly, there is lack of further consideration on how to fully utilize comments of products from multiple dimensions for recommendation. Secondly, how to mine co-preference relationship and combine it with multidimensional comments for recommendation is seldom considered. Therefore, a novel recommendation algorithm is proposed, which mines the comments from multiple dimensions and then converges it with co-preference relationship for recommendation. Experiments conducted on two real-world datasets reveal that our proposed method improves the accuracy in terms of MAE and RMSE, compared with state-of-the-art algorithms.