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 co

SUBMITTER: Cheng W 

PROVIDER: S-EPMC4873175 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature