Improved collaborative filtering recommendations using quantitative implication rules mining in implication field

Abstract

Collaborative filtering recommendation based on association rule mining has become a research trend in the field of recommender systems. However, most research results only focus on binary data, whereas in practice sets of transactions are usually quantitative data. Moreover, association rule mining algorithms are designed to focus on optimizing for basket analysis, so that in order to better serve for recommendation, they need to be adjusted. Therefore, a solution for recommender systems to deal with association rules on both binary and quantitative data as well as improve the quality of recommendation based on the rule set is a challenge today. This paper proposes a new approach to improve the accuracy, the performance and the time of recommendation by the model based on quantitative implication rules mining in the implication field.