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Time-aware tensor factorization for temporal recommendation

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Abstract

In recent years, temporal recommendation, which recommends items to users with considering temporal information has attracted widespread attention. How to capture and combine the time-varying user behavior distributions and the time-varying user behavior transition patterns is challenging. To address these challenges, we propose a Time-Aware Tensor Factorization for Temporal Recommendation (TATF4TRec). First, the personalized Markov transition tensors are applied to represent the users’ temporal behaviors. Then a tensor factorization method is proposed to capture the time-varying patterns of these tensors. Furthermore, the model linearly combines the time-varying patterns of user behavior and predicts the recommended results at a given time. Extensive experiments on five datasets demonstrate that TATF4TRec outperforms the state-of-the-art baselines significantly.

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Data Availability

The datasets used are publicly available.

Notes

  1. https://sites.google.com/site/yangdingqi/home/foursquare-dataset

  2. https://sites.google.com/site/yangdingqi/home/foursquare-dataset

  3. https://www.yongliu.org/datasets/

  4. https://snap.stanford.edu/data/loc-gowalla.html

  5. https://sites.google.com/site/yangdingqi/home/foursquare-dataset

  6. https://github.com/Ealifly/TATF4TRec

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Acknowledgements

This work is financially supported by Natural Science Foundation of Guangdong Province (2021A1515011965), National Natural Science Foundation of China (61976052), National Science Fund for Excellent Young Scholars (62122022), National Key R&D Program of China (2021ZD0111501).

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Authors

Contributions

Yali Feng designed the methodology, performed the formal analysis, and developed the software. Wen Wen provided supervision and project administration. Zhifeng Hao performed the formal analysis and gave the funding acquisition. Ruichu Cai was responsible for data curation.

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Correspondence to Yali Feng.

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Feng, Y., Wen, W., Hao, Z. et al. Time-aware tensor factorization for temporal recommendation. Appl Intell 55, 36 (2025). https://doi.org/10.1007/s10489-024-05851-x

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