{"id":"https://openalex.org/W4414034942","doi":"https://doi.org/10.1145/3705328.3759306","title":"SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead","display_name":"SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead","publication_year":2025,"publication_date":"2025-09-06","ids":{"openalex":"https://openalex.org/W4414034942","doi":"https://doi.org/10.1145/3705328.3759306"},"language":"en","primary_location":{"id":"doi:10.1145/3705328.3759306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3759306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3759306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3759306","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002049317","display_name":"Aayush Singha Roy","orcid":"https://orcid.org/0009-0000-7085-3306"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"Aayush Roy","raw_affiliation_strings":["University College Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0009-0000-7085-3306","affiliations":[{"raw_affiliation_string":"University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038973041","display_name":"\u0397\u03bb\u03af\u03b1\u03c2 \u03a4\u03c1\u03ac\u03b3\u03bf\u03c2","orcid":"https://orcid.org/0000-0001-9566-531X"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Elias Tragos","raw_affiliation_strings":["University College Dublin, Dubin, Ireland"],"raw_orcid":"https://orcid.org/0000-0001-9566-531X","affiliations":[{"raw_affiliation_string":"University College Dublin, Dubin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079971230","display_name":"Aonghus Lawlor","orcid":"https://orcid.org/0000-0002-6160-4639"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Aonghus Lawlor","raw_affiliation_strings":["University College Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-6160-4639","affiliations":[{"raw_affiliation_string":"University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084111561","display_name":"Neil Hurley","orcid":"https://orcid.org/0000-0001-8428-2866"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Neil Hurley","raw_affiliation_strings":["University College Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0001-8428-2866","affiliations":[{"raw_affiliation_string":"University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002049317"],"corresponding_institution_ids":["https://openalex.org/I100930933"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34219151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1302","last_page":"1306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8327540159225464},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.792668342590332},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6271331310272217},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6027988791465759},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5950146317481995},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.4652705192565918},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3358660936355591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1641419231891632},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.16004535555839539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8327540159225464},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.792668342590332},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6271331310272217},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6027988791465759},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5950146317481995},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.4652705192565918},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3358660936355591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1641419231891632},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.16004535555839539}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3705328.3759306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3759306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3759306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3705328.3759306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3759306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3759306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414034942.pdf","grobid_xml":"https://content.openalex.org/works/W4414034942.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2086161653","https://openalex.org/W2111094216","https://openalex.org/W2197919320","https://openalex.org/W2210543184","https://openalex.org/W2788862220","https://openalex.org/W3034503922","https://openalex.org/W4317837964","https://openalex.org/W4319792126","https://openalex.org/W4386728814","https://openalex.org/W4409944786"],"related_works":["https://openalex.org/W4230197055","https://openalex.org/W4296749040","https://openalex.org/W4404605447","https://openalex.org/W621808327","https://openalex.org/W644007644","https://openalex.org/W2497198634","https://openalex.org/W3012257603","https://openalex.org/W1586784764","https://openalex.org/W4292264782","https://openalex.org/W1559289099"],"abstract_inverted_index":{"Session-based":[0],"slate":[1,45,57,65,115],"recommendation":[2,125,168],"systems":[3],"curate":[4],"ranked":[5],"sets":[6],"of":[7,53,71,159],"items":[8],"in":[9,28,69,114,121],"real-time,":[10],"adapting":[11],"to":[12,37,43,107,154],"evolving":[13],"user":[14,109],"interactions.Balancing":[15],"relevance,":[16,105],"diversity,":[17,74],"and":[18,67,75,85,138,173],"novelty":[19],"remains":[20],"challenging":[21],"for":[22],"reinforcement":[23],"learning":[24],"(RL)":[25],"methods.Recent":[26],"advances":[27],"large":[29],"language":[30],"models":[31],"(LLMs)":[32],"offer":[33],"a":[34,87],"new":[35],"possibility":[36],"leverage":[38],"their":[39],"semantic":[40],"reasoning":[41,55,99,161],"capabilities":[42],"refine":[44,124],"composition.In":[46],"this":[47],"work,":[48],"we":[49,117],"examine":[50],"the":[51,78,152,156],"impact":[52],"LLM-driven":[54],"on":[56],"generation":[58],"by":[59,135],"integrating":[60,146],"LLMs":[61,123],"with":[62,81],"an":[63],"RL-based":[64],"recommender":[66],"evaluating":[68],"terms":[70],"accuracy,":[72],"similarity,":[73],"novelty.We":[76],"extend":[77],"RecSim":[79],"framework":[80],"real-world":[82],"interaction":[83],"data":[84],"introduce":[86],"session-aware":[88],"evaluation":[89],"protocol":[90],"that":[91,97,145],"captures":[92],"long-term":[93],"engagement.Our":[94],"analysis":[95],"reveals":[96],"LLM":[98,128,147,160],"enhances":[100],"subcategory-level":[101],"diversity":[102],"while":[103],"maintaining":[104],"leading":[106],"increased":[108],"engagement.By":[110],"visualizing":[111],"category-level":[112],"shifts":[113],"composition":[116],"uncover":[118],"systematic":[119],"patterns":[120],"how":[122],"diversity.Although":[126],"direct":[127],"use":[129],"during":[130,149],"inference":[131,164],"may":[132],"be":[133],"hampered":[134],"computational":[136],"demands":[137],"latency":[139],"concerns,":[140],"our":[141],"experimental":[142],"results":[143],"demonstrate":[144],"modifications":[148],"training":[150],"enables":[151],"model":[153],"internalize":[155],"nuanced":[157],"characteristics":[158],"without":[162],"incurring":[163],"overhead,":[165],"thereby":[166],"improving":[167],"performance,":[169],"serving":[170],"time":[171],"efficiency,":[172],"deployability.":[174]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
