Improve the algorithmic performance of collaborative filtering by using the interevent time distribution of human behaviors

Chun-Xiao Jia and Run-Ran Liu

Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, issue C, 236-245

Abstract: Recently, many scaling laws of interevent time distribution of human behaviors are observed and some quantitative understanding of human behaviors are also provided by researchers. In this paper, we propose a modified collaborative filtering algorithm by making use the scaling law of human behaviors for information filtering. Extensive experimental analyses demonstrate that the accuracies on MovieLensand Last.fm datasets could be improved greatly, compared with the standard collaborative filtering. Surprisingly, further statistical analyses suggest that the present algorithm could simultaneously improve the novelty and diversity of recommendations. This work provides a creditable way for highly efficient information filtering.

Keywords: Collaborative filtering; Interevent time distribution; Bipartite network (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:436:y:2015:i:c:p:236-245

DOI: 10.1016/j.physa.2015.05.060

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