সংরক্ষণ করুন:
| প্রধান লেখক: | , , , , , , , , |
|---|---|
| বিন্যাস: | Preprint |
| প্রকাশিত: |
2024
|
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://arxiv.org/abs/2404.00621 |
| ট্যাগগুলো: |
ট্যাগ যুক্ত করুন
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
|
| _version_ | 1866914856978874368 |
|---|---|
| author | Liu, Qijiong Zhu, Jieming Yang, Yanting Dai, Quanyu Du, Zhaocheng Wu, Xiao-Ming Zhao, Zhou Zhang, Rui Dong, Zhenhua |
| author_facet | Liu, Qijiong Zhu, Jieming Yang, Yanting Dai, Quanyu Du, Zhaocheng Wu, Xiao-Ming Zhao, Zhou Zhang, Rui Dong, Zhenhua |
| contents | Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey Liu, Qijiong Zhu, Jieming Yang, Yanting Dai, Quanyu Du, Zhaocheng Wu, Xiao-Ming Zhao, Zhou Zhang, Rui Dong, Zhenhua Information Retrieval Multimedia Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape. |
| title | Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey |
| topic | Information Retrieval Multimedia |
| url | https://arxiv.org/abs/2404.00621 |