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With the advancement of global digitization, the Internet multimedia industry has maintained steady and rapid development. Mainstream media platforms such as music and video websites, social networking software, and major Audio/Video rating databases have become an inseparable part of people’s daily life. For example, people upload homemade videos on video websites or watch platform-recommended content on Audio/Video rating websites. However, in these two scenarios, users face the following problems: 1. It is hard to find the right background music when editing videos; 2. The recommendation mode of Audio/Video database platforms is too homogeneous. This paper will combine the above specific scenarios, take the video and music matching problem as the entry point and propose the corresponding algorithm model. To improve the recommendation mechanism of multimedia websites, a cross-domain audio-video recommendation model KATLN, was proposed by integrating a joint attention mechanism with an adaptive adversarial network. Through experimental tests, this paper demonstrates the effectiveness and flexibility of the model. Cross-domain recommendation experiments show that the results are better than the current mainstream cross-domain recommendation algorithms.

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Research on Cross-Modal Recommendation System Based on Deep Neural Network

  • 会议论文

  • 发表日期：

    2025年

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  • Yan Gao,
  • Xiaobing Li,
  • Jingge Zhao,
  • Yuan Zhang,
  • Yun Tie

摘要

With the advancement of global digitization, the Internet multimedia industry has maintained steady and rapid development. Mainstream media platforms such as music and video websites, social networking software, and major Audio/Video rating databases have become an inseparable part of people’s daily life. For example, people upload homemade videos on video websites or watch platform-recommended content on Audio/Video rating websites. However, in these two scenarios, users face the following problems: 1. It is hard to find the right background music when editing videos; 2. The recommendation mode of Audio/Video database platforms is too homogeneous. This paper will combine the above specific scenarios, take the video and music matching problem as the entry point and propose the corresponding algorithm model. To improve the recommendation mechanism of multimedia websites, a cross-domain audio-video recommendation model KATLN, was proposed by integrating a joint attention mechanism with an adaptive adversarial network. Through experimental tests, this paper demonstrates the effectiveness and flexibility of the model. Cross-domain recommendation experiments show that the results are better than the current mainstream cross-domain recommendation algorithms.