{"id":"https://openalex.org/W7168404344","doi":"https://doi.org/10.1145/3805712.3809636","title":"Debiased Recommendation Beyond the Positive Propensity Assumption","display_name":"Debiased Recommendation Beyond the Positive Propensity Assumption","publication_year":2026,"publication_date":"2026-07-15","ids":{"openalex":"https://openalex.org/W7168404344","doi":"https://doi.org/10.1145/3805712.3809636"},"language":null,"primary_location":{"id":"doi:10.1145/3805712.3809636","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809636","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th 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":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805712.3809636","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140816639","display_name":"Yanghao Xiao","orcid":"https://orcid.org/0000-0001-9929-4448"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanghao Xiao","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9929-4448","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140920858","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3243-487X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3243-487X","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140898519","display_name":"Xiang Li","orcid":"https://orcid.org/0009-0007-3925-5878"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-3925-5878","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140795289","display_name":"Qian Zou","orcid":"https://orcid.org/0009-0008-4422-9735"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Zou","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-4422-9735","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140875873","display_name":"Cheng Bing","orcid":"https://orcid.org/0009-0007-8390-8717"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Bing","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-8390-8717","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140771460","display_name":"Wei Lin","orcid":"https://orcid.org/0000-0003-2851-820X"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Lin","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2851-820X","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140903335","display_name":"Haoxuan Li","orcid":"https://orcid.org/0000-0003-3620-3769"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoxuan Li","raw_affiliation_strings":["State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3620-3769","affiliations":[{"raw_affiliation_string":"State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5140839658","display_name":"Zhouchen Lin","orcid":"https://orcid.org/0000-0003-1493-7569"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouchen Lin","raw_affiliation_strings":["State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1493-7569","affiliations":[{"raw_affiliation_string":"State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":"2061","last_page":"2071"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.30649998784065247},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.2858999967575073},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.2345999926328659},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.21570000052452087},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.2134999930858612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3727000057697296},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3424000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32690000534057617},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.24279999732971191},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2345999926328659},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.21570000052452087},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2134999930858612},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.20810000598430634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805712.3809636","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809636","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805712.3809636","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809636","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1992665562","https://openalex.org/W2020631728","https://openalex.org/W2296719434","https://openalex.org/W2475334473","https://openalex.org/W2512971201","https://openalex.org/W2723293840","https://openalex.org/W2955624969","https://openalex.org/W2962989965","https://openalex.org/W2963450292","https://openalex.org/W2964983698","https://openalex.org/W2998534896","https://openalex.org/W3012576969","https://openalex.org/W3093945404","https://openalex.org/W3114569718","https://openalex.org/W3153906321","https://openalex.org/W3177379791","https://openalex.org/W3199916614","https://openalex.org/W4223591050","https://openalex.org/W4224317071","https://openalex.org/W4290927708","https://openalex.org/W4292423901","https://openalex.org/W4366460231","https://openalex.org/W4387846482","https://openalex.org/W4396757563","https://openalex.org/W4401856737","https://openalex.org/W4401857078","https://openalex.org/W4409149957","https://openalex.org/W4412378073","https://openalex.org/W7167898807","https://openalex.org/W7167915736"],"related_works":[],"abstract_inverted_index":{"Post-click":[0],"conversion":[1],"rate":[2],"(CVR)":[3],"prediction":[4,216],"is":[5,187,195],"a":[6,16,73,147],"central":[7],"task":[8],"in":[9,87,137],"recommender":[10],"systems,":[11],"yet":[12],"selection":[13,32],"bias":[14],"creates":[15],"severe":[17],"distributional":[18],"gap":[19],"between":[20],"the":[21,26,53,170,178],"clicked":[22],"training":[23,59],"samples":[24,116,153,186,194,220],"and":[25,42,120,140,164,206,221],"entire":[27],"inference":[28],"space.":[29],"To":[30,125],"address":[31],"bias,":[33],"propensity-based":[34],"methods":[35],"such":[36,80,111],"as":[37],"inverse":[38],"propensity":[39,148,157],"scoring":[40],"(IPS)":[41],"doubly":[43],"robust":[44],"(DR)":[45],"have":[46],"been":[47],"adopted,":[48],"which":[49,133],"aim":[50],"to":[51,150],"estimate":[52,160],"unbiased":[54,190],"learning":[55,171,183,191],"objective":[56,172],"from":[57],"biased":[58],"samples.":[60,142,175],"However,":[61],"these":[62],"approaches":[63],"assume":[64],"strictly":[65],"positive":[66],"propensities,":[67],"implying":[68],"every":[69],"user-item":[70],"pair":[71],"has":[72],"nonzero":[74],"probability":[75],"of":[76,169],"interaction.":[77],"In":[78,103],"practice,":[79],"positivity":[81],"assumption":[82],"maybe":[83],"violated,":[84],"for":[85,101,173],"example,":[86],"food-delivery":[88],"platforms,":[89],"some":[90],"restaurants":[91],"located":[92],"more":[93],"than":[94],"10":[95],"kilometers":[96],"away":[97],"will":[98],"be":[99],"blocked":[100],"recommendation.":[102],"this":[104,127],"study,":[105],"we":[106,129,144],"theoretically":[107],"show":[108,211],"that":[109,212],"when":[110],"zero-propensity":[112],"samples,":[113],"termed":[114],"extrapolation":[115,141,152,174,185,219],"exist,":[117],"both":[118,138],"IPS":[119],"DR":[121],"estimators":[122],"become":[123],"biased.":[124],"overcome":[126],"limitation,":[128],"propose":[130],"ExtraDebias":[131,213],"method,":[132],"enables":[134],"debiased":[135,182],"recommendation":[136],"non-extrapolation":[139,193],"Specifically,":[143],"first":[145],"train":[146],"model":[149],"identify":[151],"with":[154],"extremely":[155],"small":[156],"estimates,":[158],"then":[159],"their":[161],"pseudo-label":[162],"intervals,":[163],"derive":[165],"an":[166],"upper":[167,180],"bound":[168],"By":[176],"minimizing":[177],"derived":[179],"bound,":[181],"on":[184,192,201,218],"ensured,":[188],"while":[189],"achieved":[196],"by":[197],"standard":[198],"IPS.":[199],"Experiments":[200],"four":[202],"real-world":[203],"offline":[204],"datasets":[205],"one":[207],"online":[208],"A/B":[209],"test":[210],"effectively":[214],"minimizes":[215],"errors":[217],"achieves":[222],"optimal":[223],"performance.":[224]},"counts_by_year":[],"updated_date":"2026-07-17T05:52:16.776730","created_date":"2026-07-16T00:00:00"}
