{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T08:57:45Z","timestamp":1771577865454,"version":"3.50.1"},"reference-count":38,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,11]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ijicc-06-2024-0266","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T09:08:59Z","timestamp":1727255339000},"page":"657-670","source":"Crossref","is-referenced-by-count":6,"title":["Hypergraph contrastive learning for recommendation with side information"],"prefix":"10.1108","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0406-9737","authenticated-orcid":false,"given":"Dun","family":"Ao","sequence":"first","affiliation":[{"name":"Beijing University of Technology , Beijing,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0147-4319","authenticated-orcid":false,"given":"Qian","family":"Cao","sequence":"additional","affiliation":[{"name":"Beijing University of Technology , Beijing,","place":["China"]}]},{"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Technology , Beijing,","place":["China"]}]}],"member":"140","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"issue":"10","key":"2025102222535543000_ref001","doi-asserted-by":"publisher","first-page":"10775","DOI":"10.1609\/aaai.v38i10.28950","article-title":"No prejudice! 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