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Recommendation over time: a probabilistic model of time-aware recommender systems

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Abstract

In time-aware recommender systems, we have to consider the dynamic aspect of recommendation that is fond of new coming data. Usually, the recent data is more closely related to current recommendation tasks and the early data are useful to indicate overall measurements of the preferences. We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering systems. Our model is dynamic in the sense that it updates every time receiving new data. The time cost of every updating has a constant limit, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61672049, 61732001).

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Correspondence to Zuoquan Lin.

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Cite this article

Lin, Z., Chen, H. Recommendation over time: a probabilistic model of time-aware recommender systems. Sci. China Inf. Sci. 62, 212105 (2019). https://doi.org/10.1007/s11432-018-9915-8

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Keywords

  • recommender system
  • collaborative filtering
  • hidden Markov model
  • precision
  • cold start