TaDb: A time-aware diffusion-based recommender algorithm

Wen-Jun Li, Yuan-Yuan Xu, Qiang Dong (), Jun-Lin Zhou and Yan Fu
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Wen-Jun Li: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
Yuan-Yuan Xu: Sichuan Institute of Computer Science, Chengdu 610041, P. R. China
Qiang Dong: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China;
Jun-Lin Zhou: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
Yan Fu: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China;

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 09, 1-12

Abstract: Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose atime-aware diffusion-based(TaDb) recommender algorithm, which assigns different temporal weights to theleading linksexisting before the target user's collection and thefollowing linksappearing after that in the diffusion process. Experiments on four real datasets,Netflix,MovieLens,FriendFeedandDeliciousshow that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.

Keywords: Recommender system; temporal interest; mass diffusion; TaDb algorithm; 89.75.Hc; 87.23.Ge; 05.70.Ln (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183115501028

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