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Time-aware tensor factorization for temporal recommendation
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DOI：10.1007/s10489-024-05851-x.png)
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摘要
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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.
Keyword:
Temporal recommendation
Time-varying pattern
Tensor factorization
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3.5
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7.6K
被引数:
1.7W
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Factorizing time-heterogeneous Markov transition for temporal recommendation?.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
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