Abstract
The increasing prevalence of micro-videos on the Internet requires efficient recommendation mechanisms to help users to find interesting micro-videos. In this paper, we propose a novel personalized micro-video recommendation method using hierarchical user interest modeling based on multi-modal features. Specifically, multi-modal features, including visual, acoustic, textual, emotional and social features, are extracted from micro-videos to model user interests on three levels. The user interest scores on different levels are fused to recommend the micro-videos satisfying users’ personalized interests. The experimental results on a micro-video dataset crawled from Vine show that our method outperforms the state-of-the-art methods.
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Acknowledgements
This work is supported by National Science Foundation of China (61321491, 61202320), and Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Huang, L., Luo, B. (2018). Personalized Micro-Video Recommendation via Hierarchical User Interest Modeling. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_54
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DOI: https://doi.org/10.1007/978-3-319-77380-3_54
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