Abstract
Personalized multi-modal micro-video recommendation has attracted increasing research interests recently. Despite existing methods have achieved much progress, they ignore the importance of the user’s modality preference for micro-video recommendation. In this paper, we explore the preference-aware modality representation learning and dynamic modality information fusion, and respectively present coarse- and fine-grained modeling approaches for each aspect. Moreover, we conduct extensive experiments by integrating these approaches into existing recommendation systems, and the results demonstrate that our proposed methods can significantly improve their performance.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China, No.: 62006142; the Shandong Provincial Natural Science Foundation for Distinguished Young Scholars, No.: ZR2021JQ26; the Major Basic Research Project of Natural Science Foundation of Shandong Province, No.: ZR2021ZD15; Science and Technology Innovation Program for Distinguished Young Scholars of Shandong Province Higher Education In-stitutions, No.: 2021KJ036.
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Tian, C., Liu, M., Zhou, D. (2022). Preference-Aware Modality Representation and Fusion for Micro-video Recommendation. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_26
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DOI: https://doi.org/10.1007/978-3-031-18907-4_26
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