期刊
KNOWLEDGE-BASED SYSTEMS
卷 128, 期 -, 页码 71-77出版社
ELSEVIER
DOI: 10.1016/j.knosys.2017.04.011
关键词
Context-aware recommendation; Tensor factorization; Regression tree; Context features selection
资金
- Qingdao Science and Technology Development Project [KJZD-13-29-JCH]
- Key science and technology project of Qingdao Economic and Technological Development Zone [2013-1-25]
- National Nature Science Foundation of China [71403151, 61502278]
- Shandong University of Science and Technology Graduate Student Technology Innovation Fund [YC150217]
In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recominenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine. (C) 2017 The Author(s). Published by Elsevier B.V.
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