{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:57:23Z","timestamp":1773802643176,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>A multimodal recommendation system (MRS), which leverages rich multimodal information to model user preferences, has recently attracted significant research interest. Most existing MRSs focus primarily on developing sophisticated encoders for feature extraction, typically relying on simple aggregation of interaction-based features for final predictions. However, this conventional paradigm fails to account for the critical semantic difference between high- and low-rating interactions: while high ratings indicate user preference, low ratings explicitly convey dissatisfaction. Such oversight of negative feedback semantics may significantly limit the system\u2019s recommendation performance. Recently, sign graphs\u2014which model positive and negative feedback signals separately\u2014have gained considerable attention. Inspired by this approach, we propose Sign-Aware Multimodal Graph Recommendation (SiMGR), a novel framework incorporating signed graphs into multimodal recommendation systems. SiMGR fuses multimodal features with signed interactions in a unified graph framework by integrating modality-specific representations and applying user-level thresholds to separate positive and negative subgraphs. A balanced pseudo-edge augmentation strategy is introduced to alleviate sparsity and enhance generalization. Experiments on three public multimodal recommendation datasets show that SiMGR outperforms state-of-the-art baselines, achieving an average 4.28% improvement in NDCG@20.<\/jats:p>","DOI":"10.1609\/aaai.v40i18.38547","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:39:24Z","timestamp":1773794364000},"page":"15225-15233","source":"Crossref","is-referenced-by-count":0,"title":["Sign-Aware Multimodal Graph Recommendation"],"prefix":"10.1609","volume":"40","author":[{"given":"Yahong","family":"Lian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyao","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingjian","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"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\/38547\/42509","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38547\/42509","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:39:25Z","timestamp":1773794365000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i18.38547","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]]}}}