{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/umair-fps-user-aware-multi-modal-animation","title":"UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style","arxiv_id":"2402.10381","date":"2024-02-16","proceeding":null,"authors":["Yan Kang","Hao Lin","Mingjian Yang","Shin-Jye Lee"],"abstract":"The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users within massive data has become a challenging and popular task. However, existing anime recommendation systems have focused on text features but still need to integrate image features. In addition, most multi-modal recommendation research is constrained by tightly coupled datasets, limiting its applicability to anime illustrations. We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of domain text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight multi-modal features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree multi-modal crosses effectively. UMAIR-FPS surpasses the stat-of-the-art baselines on large real-world datasets, demonstrating substantial performance enhancements.","url_abs":"https://arxiv.org/abs/2402.10381v2","url_pdf":"https://arxiv.org/pdf/2402.10381v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"umair-fps-user-aware-multi-modal-animation","repo_url":"https://github.com/oysterqaq/umair-fps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"multi-modal-recommendation","task_name":"Multi-modal Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"dcn-v2","method_name":"DCN-V2"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}