Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.14912 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909746428116992 |
|---|---|
| author | Guan, Yalong Chen, Xiang Wang, Mingyang Wu, Xiangyu Liu, Lihao Qi, Chao Yang, Shuang Gao, Tingting Zhou, Guorui Chen, Changjian |
| author_facet | Guan, Yalong Chen, Xiang Wang, Mingyang Wu, Xiangyu Liu, Lihao Qi, Chao Yang, Shuang Gao, Tingting Zhou, Guorui Chen, Changjian |
| contents | With the rapid growth of live streaming platforms, personalized recommendation systems have become pivotal in improving user experience and driving platform revenue. The dynamic and multimodal nature of live streaming content (e.g., visual, audio, textual data) requires joint modeling of user behavior and multimodal features to capture evolving author characteristics. However, traditional methods relying on single-modal features or treating multimodal ones as supplementary struggle to align users' dynamic preferences with authors' multimodal attributes, limiting accuracy and interpretability. To address this, we propose MSPA (Multimodal Self-Corrective Preference Alignment), a personalized author recommendation framework with two components: (1) a Multimodal Preference Composer that uses MLLMs to generate structured preference text and embeddings from users' tipping history; and (2) a Self-Corrective Preference Alignment Recommender that aligns these preferences with authors' multimodal features to improve accuracy and interpretability. Extensive experiments and visualizations show that MSPA significantly improves accuracy, recall, and text quality, outperforming baselines in dynamic live streaming scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Recommendation via Self-Corrective Preference Alignmen Guan, Yalong Chen, Xiang Wang, Mingyang Wu, Xiangyu Liu, Lihao Qi, Chao Yang, Shuang Gao, Tingting Zhou, Guorui Chen, Changjian Information Retrieval With the rapid growth of live streaming platforms, personalized recommendation systems have become pivotal in improving user experience and driving platform revenue. The dynamic and multimodal nature of live streaming content (e.g., visual, audio, textual data) requires joint modeling of user behavior and multimodal features to capture evolving author characteristics. However, traditional methods relying on single-modal features or treating multimodal ones as supplementary struggle to align users' dynamic preferences with authors' multimodal attributes, limiting accuracy and interpretability. To address this, we propose MSPA (Multimodal Self-Corrective Preference Alignment), a personalized author recommendation framework with two components: (1) a Multimodal Preference Composer that uses MLLMs to generate structured preference text and embeddings from users' tipping history; and (2) a Self-Corrective Preference Alignment Recommender that aligns these preferences with authors' multimodal features to improve accuracy and interpretability. Extensive experiments and visualizations show that MSPA significantly improves accuracy, recall, and text quality, outperforming baselines in dynamic live streaming scenarios. |
| title | Multimodal Recommendation via Self-Corrective Preference Alignmen |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.14912 |