Improvement of LDA model with time factor for collaborative filtering  

作　　者：Yao Wenbin Hu Fangyi 

机构地区：[1]Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]National Engineering Laboratory for Mobile Network,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2019年第6期54-62,共9页中国邮电高校学报（英文版）

摘　　要：Collaborative filtering(CF) is one of the most widely used Algorithm in recommender systems, which help users obtain the information they may like. We proposed a latent Dirichlet allocation(LDA) model combining time and rating(TR-LDA) for CF. We use mathematical methods to fit the Ebbinghaus forgetting curve in our method and introduce time weights based on time window to find out the impact of time on user’s interests. The user’s choice of items is not only influenced by his/her interests, but also influenced by other’s rating. According to the users’ feedback, we find their rating distribution on items under the interests. Finally, experimental results on real data sets MovieLens 100 k and MovieLens 1 M show that the proposed Algorithm can predict the user implicit interests effectively and improve the recommendation performance.Collaborative filtering(CF) is one of the most widely used Algorithm in recommender systems, which help users obtain the information they may like. We proposed a latent Dirichlet allocation(LDA) model combining time and rating(TR-LDA) for CF. We use mathematical methods to fit the Ebbinghaus forgetting curve in our method and introduce time weights based on time window to find out the impact of time on user’s interests. The user’s choice of items is not only influenced by his/her interests, but also influenced by other’s rating. According to the users’ feedback, we find their rating distribution on items under the interests. Finally, experimental results on real data sets MovieLens 100 k and MovieLens 1 M show that the proposed Algorithm can predict the user implicit interests effectively and improve the recommendation performance.

关 键 词：CF LDA FORGETTING CURVE time WINDOW FEEDBACK 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]