Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2018

Abstract

Rating-only collaborative filtering has been extensively studied for decades with great improvements achieved in predicting a user’s preference on a target item at a particular time point. Yet, it remains a research challenge on how to capture users’ rating patterns which may drift over time. In this article, we propose a time-aware matrix co-factorization model, called PCCF, which considers two types of temporal effects, i.e., periodic and continual. Specifically, periodic effects refer to the impact of discrete periodic time slices with which users’ preferences may be associated, and continual effects refer to the impact of continuous gradual time over which users’ preference patterns may change. The fact that users exhibit different preference patterns with respect to different time aspect has been further confirmed by our analysis on three real-world data sets. Together with time-based user biases, we integrate the two kinds of temporal effects into a unified matrix factorization model. Experimental results on the three data sets demonstrate the effectiveness of both kinds of temporal effects for rating prediction as well as the superiority of our approach’s performance over that of the other counterparts.

Keywords

Continual effect, Periodic effect, Rating timestamps, Recommender systems, Temporal model, Co-factorization model

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Information Sciences

Volume

436-437

First Page

56

Last Page

73

ISSN

0020-0255

Identifier

10.1016/j.ins.2018.01.019

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.ins.2018.01.019

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