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- W4309344705 abstract "With the rapid development of the Internet multimedia industry, audio and video cross-modal retrieval and recommendation have become a compelling topic in deep learning. However, most methods still have certain problems. For example, it takes a lot of effort to find suitable background music for the video, and the recommendations mode of the audio-visual database platform are too singular. This article will take the matching problem of video and music as the starting point in optimizing the music retrieval mode. To improve the multimedia website recommendation mechanism, a video and audio cross-domain recommendation model KATLN is proposed. We have incorporated the ideas of domain adaptation and attention mechanism, obtained rich item feature representations, captured users’ finer-grained interest preferences, and improved the accuracy of modeling. Through experiments, it can be seen that, compared with the existing mainstream algorithms, the KATLN proposed in this paper has outstanding performance and is more prominent in accurately grasping user preferences." @default.
- W4309344705 created "2022-11-26" @default.
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- W4309344705 date "2022-10-09" @default.
- W4309344705 modified "2023-09-29" @default.
- W4309344705 title "Deep learning based audio and video cross-modal recommendation" @default.
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- W4309344705 doi "https://doi.org/10.1109/smc53654.2022.9945521" @default.
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