Effect of the time window on the heat-conduction information filtering model

Qiang Guo, Wen-Jun Song, Lei Hou, Yi-Lu Zhang and Jian-Guo Liu

Physica A: Statistical Mechanics and its Applications, 2014, vol. 401, issue C, 15-21

Abstract: Recommendation systems have been proposed to filter out the potential tastes and preferences of the normal users online, however, the physics of the time window effect on the performance is missing, which is critical for saving the memory and decreasing the computation complexity. In this paper, by gradually expanding the time window, we investigate the impact of the time window on the heat-conduction information filtering model with ten similarity measures. The experimental results on the benchmark dataset Netflix indicate that by only using approximately 11.11% recent rating records, the accuracy could be improved by an average of 33.16% and the diversity could be improved by 30.62%. In addition, the recommendation performance on the dataset MovieLens could be preserved by only considering approximately 10.91% recent records. Under the circumstance of improving the recommendation performance, our discoveries possess significant practical value by largely reducing the computational time and shortening the data storage space.

Keywords: Time window; Heat-conduction information filtering model; Bipartite network (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:401:y:2014:i:c:p:15-21

DOI: 10.1016/j.physa.2014.01.012

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