{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:58:03Z","timestamp":1773802683297,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Multimodal recommender systems have emerged as a pivotal paradigm for harnessing diverse data modalities to deliver personalized services. Contemporary research predominantly focuses on integrating heterogeneous modality information through graph learning. However, these approaches face two key challenges: (1) the inherent complexity of modalities, characterized by entangled redundant signals and noise; and (2) the challenge of effectively integrating multimodal representations, each of which may exert varying degrees of influence on users' preferences. To address these challenges, we propose a novel Collaboration-Guided Multimodal Disentanglement and Hierarchical Fusion for Recommendation (DHMRec), which simultaneously achieves intra-modal denoising disentanglement and inter-modal hierarchical fusion. Specifically, we introduce a collaboration-related modality disentanglement module to distinguish between modality-common and modality-specific features. Then, through multi-view graph learning to capture both item-item dependencies and user-item interaction patterns. Additionally, we implement hierarchical fusion between the disentangled multimodal features and ID embeddings using a positive-negative attention-aware fusion module and an interaction distribution-based alignment module. Extensive experiments  on three benchmarks demonstrate that our DHMRec surpasses various state-of-the-art baselines, highlighting its effectiveness in intra-modal disentanglement and multimodal features fusion.<\/jats:p>","DOI":"10.1609\/aaai.v40i19.38661","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:43:56Z","timestamp":1773794636000},"page":"16244-16252","source":"Crossref","is-referenced-by-count":0,"title":["DHMRec: Collaboration-Guided Multimodal Disentanglement and Hierarchical Fusion for Recommendation"],"prefix":"10.1609","volume":"40","author":[{"given":"Xiaohan","family":"Zhan","sequence":"first","affiliation":[]},{"given":"Yuliang","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Jihu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shijun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fanyu","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38661\/42623","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38661\/42623","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:43:56Z","timestamp":1773794636000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i19.38661","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}