title: Collaborative Filtering Recommendation on Users' Interest Sequences.
abstract: As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors.forename: Weijie
authors.initials: W
authors.lastname: Cheng
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors.forename: Guisheng
authors.initials: G
authors.lastname: Yin
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Dong
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Dong
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors.forename: Wansong
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D002755:Choice Behavior; D003198:Computer Simulation; D003199:Computer Systems; D003299:Cooperative Behavior; D006801:Humans; D020407:Internet; D007899:Leisure Activities; D008962:Models, Theoretical; D012660:Semantics; D012984:Software; D014584:User-Computer Interface