{"id":"https://openalex.org/W6910800429","doi":"https://doi.org/10.48550/arxiv.2405.00338","title":"Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model","display_name":"Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model","publication_year":2024,"publication_date":"2024-05-01","ids":{"openalex":"https://openalex.org/W6910800429","doi":"https://doi.org/10.48550/arxiv.2405.00338"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2405.00338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.00338","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2405.00338","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Cui, Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cui, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Feng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Pengbo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Pengbo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Bohao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bohao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tang, Heng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Heng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wan, Yi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Yi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Jun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Chen, Jiawei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiawei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.15690000355243683,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.15690000355243683,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.10639999806880951,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.07999999821186066,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6083999872207642},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5580999851226807},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5564000010490417},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5016999840736389},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.47999998927116394},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.44929999113082886},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.3709000051021576},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.36629998683929443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487000226974487},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6083999872207642},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5569000244140625},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5248000025749207},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2646999955177307},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2779119418","wikidata":"https://www.wikidata.org/wiki/Q166039","display_name":"Serendipity","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2405.00338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.00338","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2405.00338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.00338","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.849418044090271}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Owing":[0],"to":[1,46,79,92,119,139,184],"their":[2,29],"powerful":[3],"semantic":[4,87],"reasoning":[5],"capabilities,":[6],"Large":[7],"Language":[8],"Models":[9],"(LLMs)":[10],"have":[11],"been":[12],"effectively":[13],"utilized":[14],"as":[15],"recommenders,":[16],"achieving":[17],"impressive":[18],"performance.":[19],"However,":[20],"the":[21,56,65,69,77,81,94,159,164,167],"high":[22],"inference":[23],"latency":[24],"of":[25,166,179],"LLMs":[26],"significantly":[27],"restricts":[28],"practical":[30],"deployment.":[31],"To":[32,98],"address":[33],"this":[34,36,102],"issue,":[35],"work":[37,103],"investigates":[38],"knowledge":[39,58,95,113,134,150],"distillation":[40,107,114,148],"from":[41,96,115,151,158],"cumbersome":[42],"LLM-based":[43,116,186],"recommendation":[44,117],"models":[45,118,174],"lightweight":[47],"conventional":[48,120],"sequential":[49,121,173],"models.":[50,122],"It":[51],"encounters":[52],"three":[53,171],"challenges:":[54],"1)":[55,125],"teacher's":[57,82],"may":[59],"not":[60],"always":[61],"be":[62],"reliable;":[63],"2)":[64,145],"capacity":[66],"gap":[67],"between":[68],"teacher":[70,140,152],"and":[71,132,142],"student":[72,78],"makes":[73],"it":[74],"difficult":[75],"for":[76,112],"assimilate":[80],"knowledge;":[83],"3)":[84],"divergence":[85],"in":[86,188],"space":[88],"poses":[89],"a":[90,105],"challenge":[91],"distill":[93],"embeddings.":[97],"tackle":[99],"these":[100],"challenges,":[101],"proposes":[104],"novel":[106],"strategy,":[108],"DLLM2Rec,":[109,169],"specifically":[110],"tailored":[111],"DLLM2Rec":[123],"comprises:":[124],"Importance-aware":[126],"ranking":[127],"distillation,":[128],"which":[129],"filters":[130],"reliable":[131],"student-friendly":[133],"by":[135],"weighting":[136],"instances":[137],"according":[138],"confidence":[141],"student-teacher":[143],"consistency;":[144],"Collaborative":[146],"embedding":[147],"integrates":[149],"embeddings":[153],"with":[154,175],"collaborative":[155],"signals":[156],"mined":[157],"data.":[160],"Extensive":[161],"experiments":[162],"demonstrate":[163],"effectiveness":[165],"proposed":[168],"boosting":[170],"typical":[172],"an":[176],"average":[177],"improvement":[178],"47.97%,":[180],"even":[181],"enabling":[182],"them":[183],"surpass":[185],"recommenders":[187],"some":[189],"cases.":[190]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
