The Journal of China Universities of Posts and Telecommunications ›› 2019, Vol. 26 ›› Issue (6): 54-62.doi: 10.19682/j.cnki.1005-8885.2019.1026

• Artificial intelligence • Previous Articles     Next Articles

Improvement of LDA model with time factor for collaborative filtering

Yao Wenbin, Hu Fangyi   

  1. 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
  • Received:2018-04-16 Revised:2019-12-04 Online:2019-12-31 Published:2020-03-10
  • Contact: Hu Fangyi, E-mail:
  • About author:Hu Fangyi, E-mail:

Abstract: 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.

Key words:

CF, LDA, forgetting curve, time window, feedback

CLC Number: