{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bridging-language-and-items-for-retrieval-and","title":"Bridging Language and Items for Retrieval and Recommendation","arxiv_id":"2403.03952","date":"2024-03-06","proceeding":null,"authors":["Yupeng Hou","Jiacheng Li","Zhankui He","An Yan","Xiusi Chen","Julian McAuley"],"abstract":"This paper introduces BLaIR, a series of pretrained sentence embedding models specialized for recommendation scenarios. BLaIR is trained to learn correlations between item metadata and potential natural language context, which is useful for retrieving and recommending items. To pretrain BLaIR, we collect Amazon Reviews 2023, a new dataset comprising over 570 million reviews and 48 million items from 33 categories, significantly expanding beyond the scope of previous versions. We evaluate the generalization ability of BLaIR across multiple domains and tasks, including a new task named complex product search, referring to retrieving relevant items given long, complex natural language contexts. Leveraging large language models like ChatGPT, we correspondingly construct a semi-synthetic evaluation set, Amazon-C4. Empirical results on the new task, as well as conventional retrieval and recommendation tasks, demonstrate that BLaIR exhibit strong text and item representation capacity. Our datasets, code, and checkpoints are available at: https://github.com/hyp1231/AmazonReviews2023.","url_abs":"https://arxiv.org/abs/2403.03952v1","url_pdf":"https://arxiv.org/pdf/2403.03952v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bridging-language-and-items-for-retrieval-and","repo_url":"https://github.com/hyp1231/amazonreviews2023","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"}],"methods":[],"datasets_introduced":[{"slug":"amazon-baby","name":"Amazon Baby","full_name":"Amazon Baby 5-core"},{"slug":"amazon-beauty","name":"Amazon Beauty","full_name":"Amazon Beauty 5-core"},{"slug":"amazon-digital-music","name":"Amazon Digital Music","full_name":"Amazon Digital Music 5-core"},{"slug":"amazon-office-products","name":"Amazon Office Products","full_name":"Amazon Office Products 5-core"},{"slug":"amazon-toys-games","name":"Amazon Toys & Games","full_name":"Amazon Toys & Games 5-core"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2403.03952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03952"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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