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Technol."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>\n            Graph recommendation methods, representing a connected interaction perspective, reformulate user\u2013item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models (LLMs), representing a textual generative perspective, excel at modeling user languages, understanding behavioral contexts, capturing user\u2013item semantic relationships, analyzing textual sentiments, and generating coherent and contextually relevant texts as recommendations. However, there is a gap between the connected graph perspective and the text generation perspective as the task formulations are different. A research question arises: how can we effectively integrate the two perspectives for more personalized RecSys? To fill this gap, we propose to incorporate graph-edge information into LLMs via prompt and attention innovations. We reformulate recommendations as a probabilistic generative problem using prompts. We develop a framework to incorporate graph edge information from the prompt and attention mechanisms for graph-structured LLM recommendations. We develop a new prompt design that brings in both first-order and second-order graph relationships; we devise an improved LLM attention mechanism to embed direct the spatial and connectivity information of edges. Our evaluation of real-world datasets demonstrates the framework\u2019s ability to understand connectivity information in graph data and to improve the relevance and quality of recommendation results. Our code is released at:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/anord-wang\/LLM4REC.git\">https:\/\/github.com\/anord-wang\/LLM4REC.git<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3757925","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T15:23:06Z","timestamp":1754407386000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["LLM-Enhanced User\u2013Item Interactions: Leveraging Edge Information for Optimized Recommendations"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8330-342X","authenticated-orcid":false,"given":"Xinyuan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computing and AI, Arizona State University, Tempe, Arizona, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-7695","authenticated-orcid":false,"given":"Liang","family":"Wu","sequence":"additional","affiliation":[{"name":"Coupang, Mountain View, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4595-4631","authenticated-orcid":false,"given":"Liangjie","family":"Hong","sequence":"additional","affiliation":[{"name":"LinkedIn Corp, Mountain View, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona,\u00a0USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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