Collaborative Filtering Recommendation in the Implication Field

Abstract

In the age of information explosion today, the Recommender systems have become increasingly important and popular in supporting human decision-making problems. In the Recommender Systems, Collaborative filtering is one of the most popular and effective techniques available today in the recommender system. However, most of them use symmetric similarity measures. Therefore, the default effect and the role of the pair of users are the same, but in practice this may not be true. In this paper, we propose a method new approach in building the collaborative filtering recommender system in the implication field, uses the asymmetry measures to rank and filter the information to improve accurate precision of the traditional recommender systems.