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Inf. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Large language models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized\n            <jats:italic toggle=\"yes\">prompts<\/jats:italic>\n            to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results.\n          <\/jats:p>\n          <jats:p>\n            In this article, we introduce Ranker for top-\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            Recommendations (RecRanker), tailored for instruction tuning LLMs to serve as the Ranker for top-\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            Recommendations. Specifically, we introduce importance-aware sampling, clustering-based sampling, and penalty for repetitive sampling for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed\n            <jats:monospace>RecRanker<\/jats:monospace>\n            in both direct and sequential recommendation scenarios.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3705728","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T08:16:09Z","timestamp":1732868169000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8753-9137","authenticated-orcid":false,"given":"Sichun","family":"Luo","sequence":"first","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-2950","authenticated-orcid":false,"given":"Bowei","family":"He","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6084-1522","authenticated-orcid":false,"given":"Haohan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Hong Kong Institute of Science &amp; Innovation, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7531-1055","authenticated-orcid":false,"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5482-3593","authenticated-orcid":false,"given":"Yanlin","family":"Qi","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-0832","authenticated-orcid":false,"given":"Yinya","family":"Huang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4742-8624","authenticated-orcid":false,"given":"Aojun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3955-7272","authenticated-orcid":false,"given":"Yuxuan","family":"Yao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-2075","authenticated-orcid":false,"given":"Zongpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Hangdian University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5821-8569","authenticated-orcid":false,"given":"Yuanzhang","family":"Xiao","sequence":"additional","affiliation":[{"name":"University of Hawaii at Manoa, Honolulu, HI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9420-7702","authenticated-orcid":false,"given":"Mingjie","family":"Zhan","sequence":"additional","affiliation":[{"name":"Sensetime Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2756-4984","authenticated-orcid":false,"given":"Linqi","family":"Song","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Joshua Ainslie James Lee-Thorp Michiel de Jong Yury Zemlyanskiy Federico Lebr\u00f3n and Sumit Sanghai. 2023. 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