{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:09:11Z","timestamp":1757617751304,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","funder":[{"name":"Research Ireland","award":["2\/RC\/2289_P2"],"award-info":[{"award-number":["2\/RC\/2289_P2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,22]]},"DOI":"10.1145\/3705328.3759306","type":"proceedings-article","created":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:46:13Z","timestamp":1757155573000},"page":"1302-1306","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7085-3306","authenticated-orcid":false,"given":"Aayush","family":"Roy","sequence":"first","affiliation":[{"name":"University College Dublin, Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9566-531X","authenticated-orcid":false,"given":"Elias","family":"Tragos","sequence":"additional","affiliation":[{"name":"University College Dublin, Dubin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6160-4639","authenticated-orcid":false,"given":"Aonghus","family":"Lawlor","sequence":"additional","affiliation":[{"name":"University College Dublin, Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8428-2866","authenticated-orcid":false,"given":"Neil","family":"Hurley","sequence":"additional","affiliation":[{"name":"University College Dublin, Dublin, Ireland"}]}],"member":"320","published-online":{"date-parts":[[2025,9,7]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Romain Deffayet Thibaut Thonet Jean-Michel Renders and Maarten de Rijke. 2023. Generative Slate Recommendation with Reinforcement Learning. (2023).","DOI":"10.1145\/3539597.3570412"},{"key":"e_1_3_3_2_3_2","first-page":"1","volume-title":"ACM SIGIR Forum","author":"Deffayet Romain","year":"2023","unstructured":"Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, and Maarten De\u00a0Rijke. 2023. Offline evaluation for reinforcement learning-based recommendation: a critical issue and some alternatives. In ACM SIGIR Forum , Vol.\u00a056. ACM New York, NY, USA, 1\u201314."},{"key":"e_1_3_3_2_4_2","unstructured":"Gabriel Dulac-Arnold Richard Evans Hado van Hasselt Peter Sunehag Timothy Lillicrap Jonathan Hunt Timothy Mann Theophane Weber Thomas Degris and Ben Coppin. 2015. Deep reinforcement learning in large discrete action spaces. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1512.07679 (2015)."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Carlos\u00a0A Gomez-Uribe and Neil Hunt. 2015. The Netflix Recommender System: Algorithms Business Value and Innovation: ACM Transactions on Management Information Systems: Vol 6 No 4. ACM Transactions on Management Information Systems. https:\/\/dl-acmorg. ezproxy2. library. drexel. edu\/doi\/abs\/10.1145\/2843948 (2015).","DOI":"10.1145\/2843948"},{"key":"e_1_3_3_2_6_2","unstructured":"Daya Guo Dejian Yang Haowei Zhang Junxiao Song Ruoyu Zhang Runxin Xu Qihao Zhu Shirong Ma Peiyi Wang Xiao Bi et\u00a0al. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2501.12948 (2025)."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11757"},{"key":"e_1_3_3_2_8_2","unstructured":"Eugene Ie Chih-wei Hsu Martin Mladenov Vihan Jain Sanmit Narvekar Jing Wang Rui Wu and Craig Boutilier. 2019. Recsim: A configurable simulation platform for recommender systems. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1909.04847 (2019)."},{"key":"e_1_3_3_2_9_2","unstructured":"Eugene Ie Vihan Jain Jing Wang Sanmit Narvekar Ritesh Agarwal Rui Wu Heng-Tze Cheng Tushar Chandra and Craig Boutilier. 2019. SlateQ: A tractable decomposition for reinforcement learning with recommendation sets. (2019)."},{"key":"e_1_3_3_2_10_2","unstructured":"Aviral Kumar Aurick Zhou George Tucker and Sergey Levine. 2020. Conservative q-learning for offline reinforcement learning. Advances in neural information processing systems 33 (2020) 1179\u20131191."},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583244"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Boris\u00a0T Polyak and Anatoli\u00a0B Juditsky. 1992. Acceleration of stochastic approximation by averaging. SIAM journal on control and optimization 30 4 (1992) 838\u2013855.","DOI":"10.1137\/0330046"},{"key":"e_1_3_3_2_13_2","volume-title":"The International FLAIRS Conference Proceedings","volume":"36","author":"Roy Aayush\u00a0Singha","year":"2023","unstructured":"Aayush\u00a0Singha Roy, Edoardo D\u2019Amico, Aonghus Lawlor, and Neil Hurley. 2023. Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. In The International FLAIRS Conference Proceedings , Vol.\u00a036."},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","unstructured":"Aayush\u00a0Singha Roy Edoardo D\u2019Amico Elias Tragos Aonghus Lawlor and Neil Hurley. 2025. Don\u2019t Get Bored: Enhancing Scalability and Diversity in Session-Based Slate Recommendation. ACM Trans. Recomm. Syst. (April 2025). 10.1145\/3733241Just Accepted.","DOI":"10.1145\/3733241"},{"key":"e_1_3_3_2_15_2","unstructured":"Aayush\u00a0Singha Roy Elias Tragos Aonghus Lawlor and Neil Hurley. 2024. Simulating Real-World News Consumption: Deep Q-Learning for Diverse User-Centric Slate Recommendations. (2024)."},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608843"},{"key":"e_1_3_3_2_17_2","unstructured":"Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miro Dudik John Langford Damien Jose and Imed Zitouni. 2017. Off-policy evaluation for slate recommendation. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2795403.2795405"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Tao Zhou Zolt\u00e1n Kuscsik Jian-Guo Liu Mat\u00fa\u0161 Medo Joseph\u00a0Rushton Wakeling and Yi-Cheng Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107 10 (2010) 4511\u20134515.","DOI":"10.1073\/pnas.1000488107"}],"event":{"name":"RecSys '25: Nineteenth ACM Conference on Recommender Systems","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGIR ACM Special Interest Group on Information Retrieval","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Prague Czech Republic","acronym":"RecSys '25"},"container-title":["Proceedings of the Nineteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705328.3759306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:42:32Z","timestamp":1757158952000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705328.3759306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,7]]},"references-count":19,"alternative-id":["10.1145\/3705328.3759306","10.1145\/3705328"],"URL":"https:\/\/doi.org\/10.1145\/3705328.3759306","relation":{},"subject":[],"published":{"date-parts":[[2025,9,7]]},"assertion":[{"value":"2025-09-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}