动态捕捉用户兴趣变化，生成多模态个性化内容。
Temporal Interest-Driven Multimodal Personalized Content Generation
- 基于时间演化的兴趣建模，融合文本图像视频音频多模态特征。
- 联合学习时序依赖与模态偏好，统一表示用户兴趣。
- 适用于电商广告教育医疗等场景，适合做个性化推荐研究者。
随着用户兴趣的动态演变及互联网应用中多模态需求的增长，基于静态兴趣偏好的个性化内容生成策略难以满足实际应用要求。提出的TIMGen（Temporal Interest-driven Multimodal Generation）模型通过建模用户兴趣的长期时序演化，捕捉具有强时序依赖性的动态兴趣表示。该模型支持文本、图像、视频、音频等多模态特征融合，并基于多模态偏好生成定制化内容。TIMGen联合学习时序依赖与模态偏好，获得统一的兴趣表示，进而生成满足用户个性化内容需求的内容。该模型克服了基于静态偏好的个性化推荐方法的不足，实现了用户多模态兴趣的灵活与动态建模，更精准地理解与捕捉其兴趣与偏好。可扩展至电商、广告、在线教育、精准医疗等多种实际应用场景，为未来研究提供思路。
原文摘要 · Abstract (English)
With the dynamic evolution of user interests and the increasing multimodal demands in internet applications, personalized content generation strategies based on static interest preferences struggle to meet practical application requirements. The proposed TIMGen (Temporal Interest-driven Multimodal Generation) model addresses this challenge by modeling the long-term temporal evolution of users' interests and capturing dynamic interest representations with strong temporal dependencies. This model also supports the fusion of multimodal features, such as text, images, video, and audio, and delivers customized content based on multimodal preferences. TIMGen jointly learns temporal dependencies and modal preferences to obtain a unified interest representation, which it then generates to meet users' personalized content needs. TIMGen overcomes the shortcomings of personalized content recommendation methods based on static preferences, enabling flexible and dynamic modeling of users' multimodal interests, better understanding and capturing their interests and preferences. It can be extended to a variety of practical application scenarios, including e-commerce, advertising, online education, and precision medicine, providing insights for future research.
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