Abstract
Recently, with the increasing of heterogeneous information, recommender system has gradually transferred from a single view of rating to multi-dimensional information integration. However, the existing approaches cannot fully exploit the users’ information. In this paper, we propose a deep learning based framework, which uses heterogeneous information and also considers temporal changes in users’ interests to extract the users’ features. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for recommendation task.
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Acknowledgment
This work was supported by National Key Research and Development Plan (2016QY02D0402).
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Jin, Z., Zhang, Y., Mu, W., Wang, W., Jin, H. (2018). Collaborating Aesthetic Change and Heterogeneous Information into Recommender Systems. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_12
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DOI: https://doi.org/10.1007/978-3-319-97304-3_12
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