With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vas...With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study,we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.81873915,61702225 and 61806026Ministry of Science and Technology Key Research and Development Program of China under Grant No.2018YFC0116902+3 种基金by the Natural Science Foundation of Jiangsu Province under Grant No.BK20180956by the 2018 Six Talent Peaks Project of Jiangsu Province under Grant No.XYDXX-127by the Science and Technology demonstration project of social development of Wuxi under Grant WX18IVJN002by the Philosophy and Social Science Foundation of Jiangsu Province(18YSC009).
文摘With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study,we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches.
