Computer Science ›› 2017, Vol. 44 ›› Issue (4): 295-301.doi: 10.11896/j.issn.1002-137X.2017.04.060

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Research of Time Weighted Collaborative Filtering Algorithm in Movie Recommendation

LAN Yan and CAO Fang-fang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to deal with the outdated information problem of collaborative filtering algorithm,a new time weighted collaborative filtering algorithm (NTWCF) was proposed.Considering the influence of this change,it introduced the concept of information retention period which inspired by the information half-value period.At the stages of nearest neighbor searching and predictive scoring,this paper used a novel time weighted function to put time weight to the user’sscore.The experimental results of movie data sets show that it can greatly improve the accuracy of predicted ratings.Then,in order to improve the real-time speed of algorithm,this paper used time weighted item clustering algorithm to optimize NTWCF,and put forward a collaborative filtering algorithm based on time weight and item clustering (TWICCF).It used K-means to cluster the items based on time weighted scores,and then searched the nearest neighbors of the target item in the set of some clusters.TWICCF algorithm achieves significant improvements in both recommendation accuracy and real-time than NTWCF algorithm.

Key words: Collaborative filtering,Movie recommendation,Information half-value period,Information retention period,Time weighted,Item clustering

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