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Authors: [default-pr]Gustavo Penha, [default-pr]Ali Vardasbi, [contrib-99]Enrico Palumbo, [default-pr]Marco De Nadai, [default-pr] Hugues BouchardAuthors Info & Claims RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems Pages 340 - 349 https://doi.org/10.1145/3640457.3688123 Published: 08 October 2024 Publication History 0citation812Downloads Metrics Total Citations0 Total Downloads812 Last 12 Months812 Last 6 weeks812 Get Citation Alerts New Citation Alert added! This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. To manage your alert preferences, click on the button below. Manage my Alerts New Citation Alert! Please log in to your account All formatsPDF * Contents RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other? Pages 340 - 349 PREVIOUS ARTICLE Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation Previous NEXT ARTICLE Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction Next + Abstract + References ACM Digital Library * + Information & Contributors + Bibliometrics & Citations + View Options + References + Media + Tables + Share Abstract Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, generative models directly associate inputs with item IDs. Given the breakthroughs of Large Language Models (LLMs), these generative systems can play a crucial role in centralizing a variety of Information Retrieval (IR) tasks in a single model that performs tasks such as query understanding, retrieval, recommendation, explanation, re-ranking, and response generation. Despite the growing interest in such a unified generative approach for IR systems, the advantages of using a single, multi-task model over multiple specialized models are not well established in the literature. This paper investigates whether and when such a unified approach can outperform task-specific models in the IR tasks of search and recommendation, broadly co-existing in multiple industrial online platforms, such as Spotify, YouTube, and Netflix. Previous work shows that (1) the latent representations of items learned by generative recommenders are biased towards popularity, and (2) content-based and collaborative-filtering-based information can improve an item's representations. Motivated by this, our study is guided by two hypotheses: [H1] the joint training regularizes the estimation of each item's popularity, and [H2] the joint training regularizes the item's latent representations, where search captures content-based aspects of an item and recommendation captures collaborative-filtering aspects. Our extensive experiments with both simulated and real-world data support both [H1] and [H2] as key contributors to the effectiveness improvements observed in the unified search and recommendation generative models over the single-task approaches. References [1] Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024. LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. arXiv preprint arXiv:2404.05961 (2024). Google Scholar [2] Nicholas J Belkin and W Bruce Croft. 1992. Information filtering and information retrieval: Two sides of the same coin?Commun. ACM 35, 12 (1992), 29-38. 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Share on social media XLinkedInRedditFacebookemail Affiliations [default-pr] Gustavo Penha Tech Research, Spotify, Netherlands https://orcid.org/0000-0002-7373-0800 View Profile [default-pr] Ali Vardasbi Tech Research, Spotify, Netherlands https://orcid.org/0000-0002-9342-5272 View Profile [contrib-99] Enrico Palumbo Tech Research, Spotify, Italy https://orcid.org/0000-0003-3898-7480 View Profile [default-pr] Marco De Nadai Tech Research, Spotify, Denmark https://orcid.org/0000-0001-8466-3933 View Profile [default-pr] Hugues Bouchard Tech Research, Spotify, Spain https://orcid.org/0000-0003-2315-8268 View Profile Download PDF Go to Go to Show all references Request permissionsExpand All Collapse Expand Table Authors Info & Affiliations View Table of Contents Export Citations Select Citation format[BibTeX ] * Please download or close your previous search result export first before starting a new bulk export. Preview is not available. 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