{"id":"https://openalex.org/W4413979793","doi":"https://doi.org/10.1109/tkde.2025.3606149","title":"Large Language Models Meet Causal Inference: Semantic-Rich Dual Propensity Score for Sequential Recommendation","display_name":"Large Language Models Meet Causal Inference: Semantic-Rich Dual Propensity Score for Sequential Recommendation","publication_year":2025,"publication_date":"2025-09-04","ids":{"openalex":"https://openalex.org/W4413979793","doi":"https://doi.org/10.1109/tkde.2025.3606149"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2025.3606149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3606149","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","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/A5067132374","display_name":"Dianer Yu","orcid":"https://orcid.org/0000-0001-6376-9667"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dianer Yu","raw_affiliation_strings":["Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0001-6376-9667","affiliations":[{"raw_affiliation_string":"Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]},{"raw_affiliation_string":"Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100340627","display_name":"Qian Li","orcid":"https://orcid.org/0000-0002-8308-9551"},"institutions":[{"id":"https://openalex.org/I205640436","display_name":"Curtin University","ror":"https://ror.org/02n415q13","country_code":"AU","type":"education","lineage":["https://openalex.org/I205640436"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Qian Li","raw_affiliation_strings":["School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia"],"raw_orcid":"https://orcid.org/0000-0002-8308-9551","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia","institution_ids":["https://openalex.org/I205640436"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003783385","display_name":"Sirui Huang","orcid":"https://orcid.org/0009-0007-7192-973X"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sirui Huang","raw_affiliation_strings":["Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]},{"raw_affiliation_string":"Data Science and Machine Intelligence Lab, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083806438","display_name":"Jie Cao","orcid":"https://orcid.org/0000-0002-7049-5614"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Cao","raw_affiliation_strings":["School of Management, Hefei University of Technology, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-7049-5614","affiliations":[{"raw_affiliation_string":"School of Management, Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051512158","display_name":"Guandong Xu","orcid":"https://orcid.org/0000-0003-4493-6663"},"institutions":[{"id":"https://openalex.org/I4210086892","display_name":"Education University of Hong Kong","ror":"https://ror.org/000t0f062","country_code":"HK","type":"education","lineage":["https://openalex.org/I4210086892"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Guandong Xu","raw_affiliation_strings":["Centre for Learning, Teaching &#x0026; Technology, Education University of Hong Kong, Hong Kong","Centre for Learning, Teaching &amp; Technology, The Education University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-4493-6663","affiliations":[{"raw_affiliation_string":"Centre for Learning, Teaching &#x0026; Technology, Education University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I4210086892"]},{"raw_affiliation_string":"Centre for Learning, Teaching &amp; Technology, The Education University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I4210086892"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6796,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87994845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"37","issue":"11","first_page":"6494","last_page":"6505"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9955999851226807,"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/T10028","display_name":"Topic Modeling","score":0.9955999851226807,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9898999929428101,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9690999984741211,"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/computer-science","display_name":"Computer science","score":0.8198519349098206},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6031296849250793},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.5509207248687744},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.5408139824867249},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5357334613800049},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5109697580337524},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4584067165851593},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3650023341178894},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.17937859892845154},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15156489610671997},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10779842734336853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8198519349098206},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6031296849250793},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.5509207248687744},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5408139824867249},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5357334613800049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5109697580337524},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4584067165851593},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3650023341178894},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.17937859892845154},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15156489610671997},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10779842734336853},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2025.3606149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3606149","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2729752981","display_name":"\u57fa\u4e8e\u56e0\u679c\u5206\u6790\u7684\u53ef\u89e3\u91ca\u63a8\u8350\u7cfb\u7edf\u7684\u7b97\u6cd5\u7406\u8bba\u4e0e\u5e94\u7528\u7814\u7a76","funder_award_id":"62072257","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6537946155","display_name":"Contextual Behabiour Predictions in Dynamic Mobile E-commerce","funder_award_id":"DP220103717","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W281665770","https://openalex.org/W1886704267","https://openalex.org/W2069362824","https://openalex.org/W2295739661","https://openalex.org/W2788246224","https://openalex.org/W2809307135","https://openalex.org/W2886209086","https://openalex.org/W2951431594","https://openalex.org/W2960538550","https://openalex.org/W2964044287","https://openalex.org/W2965744319","https://openalex.org/W2998534896","https://openalex.org/W3012903288","https://openalex.org/W3033844654","https://openalex.org/W3115487106","https://openalex.org/W3161460925","https://openalex.org/W3185693672","https://openalex.org/W3200027692","https://openalex.org/W3205333154","https://openalex.org/W3207257408","https://openalex.org/W4210334834","https://openalex.org/W4223982309","https://openalex.org/W4224313077","https://openalex.org/W4225356279","https://openalex.org/W4290874879","https://openalex.org/W4292419518","https://openalex.org/W4306317375","https://openalex.org/W4307168805","https://openalex.org/W4318960934","https://openalex.org/W4321480014","https://openalex.org/W4367595583","https://openalex.org/W4384659132","https://openalex.org/W4386729952","https://openalex.org/W4387415134","https://openalex.org/W4389714157","https://openalex.org/W4390490824","https://openalex.org/W4390493562","https://openalex.org/W4391136507","https://openalex.org/W4391262214","https://openalex.org/W4392367398","https://openalex.org/W4396734745","https://openalex.org/W4396758712","https://openalex.org/W4396758737","https://openalex.org/W4397031626","https://openalex.org/W4403582716"],"related_works":["https://openalex.org/W2947954788","https://openalex.org/W4296363906","https://openalex.org/W4322505266","https://openalex.org/W4281756720","https://openalex.org/W4385077270","https://openalex.org/W2541915084","https://openalex.org/W4225280467","https://openalex.org/W304115605","https://openalex.org/W4412828429","https://openalex.org/W2884906108"],"abstract_inverted_index":{"Sequential":[0],"recommender":[1],"systems":[2],"(SRSs)":[3],"are":[4],"designed":[5],"to":[6,10,28,56,146],"suggest":[7],"relevant":[8],"items":[9,43],"users":[11],"by":[12,180],"analyzing":[13],"their":[14,46],"interaction":[15],"sequences.":[16],"However,":[17],"SRSs":[18,50],"often":[19],"suffer":[20],"from":[21,138,164],"exposure":[22,31,58,63,92,178],"bias":[23,64,179],"in":[24,74,134,221],"these":[25,154],"sequences":[26],"due":[27],"imbalanced":[29],"item":[30,54,167,182],"and":[32,65,83,125,141,168,187],"varied":[33],"user":[34,169,184],"activity":[35,185],"levels,":[36,186],"creating":[37],"a":[38,75,99],"self-reinforcing":[39],"loop":[40],"favoring":[41],"popular":[42],"regardless":[44],"of":[45,78,198,223],"true":[47,210],"relevance.":[48],"Most":[49],"only":[51],"focus":[52],"on":[53],"dependencies":[55],"address":[57],"bias,":[59],"while":[60],"overlooking":[61],"user-side":[62],"the":[66,166,193,196],"rich":[67,136],"semantics":[68,137],"behind":[69],"interactions.":[70],"These":[71,171],"oversights":[72],"result":[73],"limited":[76],"understanding":[77],"less":[79,88],"active":[80],"users'":[81,209],"preferences":[82],"inaccurate":[84],"preference":[85],"capture":[86],"for":[87,206],"exposed":[89],"items,":[90],"exacerbating":[91],"biases.":[93],"Towards":[94],"this":[95],"end,":[96],"we":[97],"propose":[98],"novel":[100],"method":[101],"<bold":[102,105,108,112],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[103,106,109,113,116],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">L</b>LM-enhanced":[104],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">D</b>ual":[107],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">P</b>ropensity":[110],"Score":[111],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">E</b>stimation":[114],"(<bold":[115],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">LDPE</b>),":[117],"which":[118],"synergistically":[119],"integrates":[120,143],"Large":[121],"Language":[122],"Models":[123],"(LLMs)":[124],"causal":[126],"inference.":[127],"First,":[128],"LDPE":[129,158,191,217],"leverages":[130],"LLMs'":[131],"superior":[132],"ability":[133],"capturing":[135],"textual":[139],"data":[140],"then":[142],"collaborative":[144],"information":[145],"generate":[147],"debiased":[148,155,161],"semantic-rich":[149],"LLM-based":[150],"user/item":[151],"embeddings.":[152],"With":[153],"item/user":[156],"embeddings,":[157],"estimates":[159],"time-aware":[160],"propensity":[162,173,204],"scores":[163,174,205],"both":[165],"sides.":[170],"dual":[172,203],"can":[175],"fully":[176],"mitigate":[177],"considering":[181],"popularity,":[183],"temporal":[188],"dynamics.":[189],"Lastly,":[190],"employs":[192],"transformer":[194],"as":[195],"backbone":[197],"our":[199,216],"method,":[200],"incorporating":[201],"estimated":[202],"accurately":[207],"predicting":[208],"preferences.":[211],"Extensive":[212],"experiments":[213],"show":[214],"that":[215],"outperforms":[218],"state-of-the-art":[219],"baselines":[220],"terms":[222],"recommendation":[224],"performance.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}
