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| Effect of the Time Window on the Personalized Recommendation Algorithm |
| SONG Wenjun, GUO Qiang, LIU Jianguo
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| Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, China |
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Abstract In this paper, we investigate the effect of the time window on the personalized recommendation algorithm based on ten similarity measures. The experimental results on the benchmark dataset MovieLens indicate that by only adapting approximately 12.56% recent rating records, the accuracy could be improved by an average of 27.17%, and the diversity could be improved by 3.28%. Our work is valuable in both theory and practice, and it could largely reduce the calculation complexity triggered by massive data.
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Received: 24 November 2013
Published: 22 June 2026
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