{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:50:38Z","timestamp":1773802238273,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"17","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>In recommender systems, recent advances highlight the critical role of alignment and uniformity (AU) in representation learning. Specifically, AU-based methods pull positive user-item pairs closer (alignment) and spread the overall representation distribution (uniformity), typically relying on observed positive samples. Despite their effectiveness, exist methods face two limitations: (1) noise issues have a more severe impact on AU-based methods in the absence of negative samples, leading to the capture of spurious signals such as misclicks or non-preferential behaviors; (2) data sparsity weakens the alignment of user-item representations, hindering reliable representation learning and harming recommendations for sparse users. To tackle these issues, we propose a novel recommendation framework named Constrained Alignment and Filtered Uniformity (CAFU). CAFU enhances robustness through Filtered Uniformity (FU) and improves performance under data sparsity via Constrained Alignment (CA). Specifically, FU adopts a threshold-based strategy to eliminate unreliable samples that degrade embedding quality, thereby strengthening robustness. In parallel, CA mitigates the impact of sparsity by masking low-confidence user-item pairs based on angular distance, leading to better recommendation for sparse users. Extensive experiments on three datasets and three backbones demonstrate the effectiveness and generalization of the proposed framework.<\/jats:p>","DOI":"10.1609\/aaai.v40i17.38517","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:31:19Z","timestamp":1773793879000},"page":"14955-14963","source":"Crossref","is-referenced-by-count":0,"title":["CAFU: Constrained Alignment and Filtered Uniformity for Denoising Recommendation"],"prefix":"10.1609","volume":"40","author":[{"given":"Xinzhe","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Lei","family":"Sang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kaibin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yiwen","family":"Zhang","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\/38517\/42479","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38517\/42479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:31:19Z","timestamp":1773793879000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i17.38517","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]]}}}