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TaDb: A time-aware diffusion-based recommender algorithm

Author

Listed:
  • 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;
    Big Data Research Center, 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;
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China)

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.

Suggested Citation

  • Wen-Jun Li & Yuan-Yuan Xu & Qiang Dong & Jun-Lin Zhou & Yan Fu, 2015. "TaDb: A time-aware diffusion-based recommender algorithm," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(09), pages 1-12.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:09:n:s0129183115501028
    DOI: 10.1142/S0129183115501028
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    Cited by:

    1. Dong, Qiang & Yuan, Quan & Shi, Yang-Bo, 2019. "Alleviating the recommendation bias via rank aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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