Abstract
Data sparsity is one of the most challenging problems in recommendation systems. In this paper, we tackle this problem by proposing a novel multi-modal behavioral information-aware recommendation method named MIAR which is based on recurrent neural networks and matrix factorization. First, an interaction context-aware sequential prediction model is designed to capture user-item interaction contextual information and behavioral sequence information. Second, an attributed context-aware rating prediction model is proposed to capture attribution contextual information and rating information. Finally, three fusion methods are developed to combine two sub-models. As a result, the MIAR method has several distinguished advantages in terms of mitigating the data sparsity problem. The method can well perceive diverse influences of interaction and attribution contextual information. Meanwhile, a large number of behavioral sequence and rating information can be utilized by the MIAR approach. The proposed algorithm is evaluated on real-world datasets and the experimental results show that MIAR can significantly improve recommendation performance compared to the existing state-of-art recommendation algorithms.
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Acknowledgments
This work is supported by the Chinese National Science Foundation (#61763007), the Guilin Science and Technology Project (20170113-6) and the Guangxi Natural Science Foundation (#2017JJD160017).
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Cai, G., Chen, N., Gu, W., Pan, J. (2020). Multi-modal Behavioral Information-Aware Recommendation with Recurrent Neural Networks. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_66
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DOI: https://doi.org/10.1007/978-3-030-16946-6_66
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