{"id":"https://openalex.org/W4400524801","doi":"https://doi.org/10.1145/3626772.3657782","title":"Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models","display_name":"Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400524801","doi":"https://doi.org/10.1145/3626772.3657782"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084848859","display_name":"Yankun Ren","orcid":"https://orcid.org/0009-0008-0541-214X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yankun Ren","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0008-0541-214X","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005167651","display_name":"Zhongde Chen","orcid":"https://orcid.org/0000-0002-4949-908X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhongde Chen","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4949-908X","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101852690","display_name":"Xinxing Yang","orcid":"https://orcid.org/0009-0005-9519-4526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinxing Yang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-9519-4526","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430936","display_name":"Longfei Li","orcid":"https://orcid.org/0000-0002-9263-7011"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longfei Li","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9263-7011","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026709091","display_name":"Cong Jiang","orcid":"https://orcid.org/0000-0002-7813-969X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cong Jiang","raw_affiliation_strings":["Ant Group, Hang Zhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7813-969X","affiliations":[{"raw_affiliation_string":"Ant Group, Hang Zhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040949467","display_name":"Lei Cheng","orcid":"https://orcid.org/0009-0002-2186-699X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Cheng","raw_affiliation_strings":["Ant Group, Hang Zhou, China"],"raw_orcid":"https://orcid.org/0009-0002-2186-699X","affiliations":[{"raw_affiliation_string":"Ant Group, Hang Zhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103325601","display_name":"Bo Zhang","orcid":"https://orcid.org/0009-0007-5396-4443"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-5396-4443","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044628261","display_name":"Linjian Mo","orcid":"https://orcid.org/0000-0002-6682-1448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Linjian Mo","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6682-1448","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045140292","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-6033-6102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6033-6102","affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.4584,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.99110576,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"345","last_page":"354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9990000128746033,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9911999702453613,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7564942836761475},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5119527578353882},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.419717401266098}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7564942836761475},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5119527578353882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.419717401266098}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626772.3657782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2030808931","https://openalex.org/W2171279286","https://openalex.org/W2295739661","https://openalex.org/W2783272285","https://openalex.org/W2902040508","https://openalex.org/W2963367478","https://openalex.org/W2964044287","https://openalex.org/W2964296635","https://openalex.org/W2984100107","https://openalex.org/W3065542300","https://openalex.org/W3100260481","https://openalex.org/W3156844209","https://openalex.org/W3208227120","https://openalex.org/W4221143046","https://openalex.org/W4226278401","https://openalex.org/W4284711612","https://openalex.org/W4288083766","https://openalex.org/W4296591867"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Recommender":[0],"systems":[1,77],"are":[2],"widely":[3],"used":[4],"in":[5,24,50,69,213],"various":[6],"online":[7],"platforms.":[8],"In":[9,94],"the":[10,21,56,64,147,182,196],"context":[11],"of":[12,86,149,198,205],"sequential":[13,33,75,87,121,124,200],"recommendation,":[14],"it":[15],"is":[16],"essential":[17],"to":[18,27,58,111,161,180,216],"accurately":[19],"capture":[20],"chronological":[22],"patterns":[23,89],"user":[25],"activities":[26],"generate":[28,217],"relevant":[29],"recommendations.":[30],"Conventional":[31],"ID-based":[32,120,143,168,199],"recommenders":[34,122,169],"have":[35],"shown":[36],"promise":[37],"but":[38],"lack":[39],"comprehensive":[40],"real-world":[41,66],"knowledge":[42,67,130,172,222],"about":[43],"items,":[44],"limiting":[45],"their":[46],"effectiveness.":[47],"Recent":[48],"advancements":[49],"Large":[51,108],"Language":[52],"Models":[53],"(LLMs)":[54],"offer":[55],"potential":[57],"bridge":[59],"this":[60,95,139],"gap":[61],"by":[62,132],"leveraging":[63],"extensive":[65],"encapsulated":[68],"LLMs.":[70],"However,":[71],"integrating":[72],"LLMs":[73,117,133,215],"into":[74,142],"recommender":[76],"comes":[78],"with":[79,103,118,134],"its":[80],"own":[81],"challenges,":[82],"including":[83],"inadequate":[84],"representation":[85],"behavior":[88],"and":[90,137,208,220],"long":[91],"inference":[92],"latency.":[93],"paper,":[96],"we":[97,153],"propose":[98],"SeRALM":[99,115,193],"(Enhancing":[100],"<u>Se</u>quential":[101],"<u>R</u>ecommenders":[102],"Augmented":[104],"Knowledge":[105],"from":[106,146,167],"<u>A</u>ligned":[107],"<u>L</u>anguage":[109],"<u>M</u>odels)":[110],"address":[112],"these":[113],"challenges.":[114],"integrates":[116],"conventional":[119],"for":[123,170],"recommendation":[125],"tasks.":[126],"We":[127,174],"combine":[128],"text-format":[129],"generated":[131],"item":[135],"IDs":[136],"feed":[138],"enriched":[140],"data":[141],"recommenders,":[144],"benefitting":[145],"strengths":[148],"both":[150],"paradigms.":[151],"Moreover,":[152],"develop":[154],"a":[155,203],"theoretically":[156],"underpinned":[157],"alignment":[158,183],"training":[159,184],"method":[160],"refine":[162],"LLMs'":[163],"generation":[164],"using":[165],"feedback":[166],"better":[171],"augmentation.":[173],"also":[175],"present":[176],"an":[177],"asynchronous":[178],"technique":[179],"expedite":[181],"process.":[185],"Experimental":[186],"results":[187],"on":[188],"public":[189],"benchmarks":[190],"demonstrate":[191],"that":[192],"significantly":[194],"improves":[195],"performances":[197],"recommenders.":[201],"Further,":[202],"series":[204],"ablation":[206],"studies":[207],"analyses":[209],"corroborate":[210],"SeRALM's":[211],"proficiency":[212],"steering":[214],"more":[218],"pertinent":[219],"advantageous":[221],"across":[223],"diverse":[224],"scenarios.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
