{"id":"https://openalex.org/W4280611174","doi":"https://doi.org/10.1145/3477495.3531975","title":"Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation","display_name":"Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4280611174","doi":"https://doi.org/10.1145/3477495.3531975"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531975","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531975","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th 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/A5007858277","display_name":"Weiming Liu","orcid":"https://orcid.org/0000-0002-4115-7667"},"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":"Weiming Liu","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074603286","display_name":"Xiaolin Zheng","orcid":"https://orcid.org/0000-0001-5483-0366"},"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":"Xiaolin Zheng","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041726953","display_name":"Jiajie Su","orcid":"https://orcid.org/0000-0002-6899-4174"},"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":"Jiajie Su","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025958474","display_name":"Mengling Hu","orcid":"https://orcid.org/0000-0002-6138-9895"},"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":"Mengling Hu","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083137715","display_name":"Yanchao Tan","orcid":"https://orcid.org/0000-0002-3526-6859"},"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":"Yanchao Tan","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028791879","display_name":"Chaochao Chen","orcid":"https://orcid.org/0000-0003-1419-964X"},"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":"Chaochao Chen","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":12.9088,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.99169731,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"312","last_page":"321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9793999791145325,"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/T10028","display_name":"Topic Modeling","score":0.9750999808311462,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7965109348297119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7932512760162354},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7795708179473877},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.604367196559906},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5627435445785522},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5596341490745544},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5402796268463135},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5369664430618286},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44009628891944885},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41834574937820435},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3858177363872528},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3522205054759979},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33215174078941345},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.19796577095985413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10103696584701538}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7965109348297119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7932512760162354},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7795708179473877},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.604367196559906},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5627435445785522},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5596341490745544},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5402796268463135},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5369664430618286},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44009628891944885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41834574937820435},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3858177363872528},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3522205054759979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33215174078941345},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.19796577095985413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10103696584701538},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3531975","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531975","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G7828322666","display_name":null,"funder_award_id":"(No.72192823 and No.62172362)","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W143867266","https://openalex.org/W385466589","https://openalex.org/W2035279617","https://openalex.org/W2115403315","https://openalex.org/W2118338035","https://openalex.org/W2131953535","https://openalex.org/W2164943005","https://openalex.org/W2187089797","https://openalex.org/W2593768305","https://openalex.org/W2605350416","https://openalex.org/W2740063619","https://openalex.org/W2740605635","https://openalex.org/W2796608345","https://openalex.org/W2808716093","https://openalex.org/W2887280559","https://openalex.org/W2899813732","https://openalex.org/W2907206525","https://openalex.org/W2945623882","https://openalex.org/W2963085847","https://openalex.org/W2971196067","https://openalex.org/W2987679642","https://openalex.org/W2997617192","https://openalex.org/W3028201915","https://openalex.org/W3041133507","https://openalex.org/W3080236009","https://openalex.org/W3098400049","https://openalex.org/W3098638686","https://openalex.org/W3099026360","https://openalex.org/W3102099102","https://openalex.org/W3115386848","https://openalex.org/W3156963027","https://openalex.org/W3175332915","https://openalex.org/W4255567990"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2970845521"],"abstract_inverted_index":{"Cross-Domain":[0,71],"Recommendation":[1,72],"(CDR)":[2],"has":[3],"been":[4],"popularly":[5],"studied":[6],"to":[7,12,78,89,119,168,181],"utilize":[8,97],"different":[9],"domain":[10],"knowledge":[11,41,100],"solve":[13],"the":[14,22,29,35,53,68,80,84,98,102,109,113,143,183,203,208],"cold-start":[15],"problem":[16],"in":[17,58],"recommender":[18],"systems.":[19],"Most":[20],"of":[21,47,82,115],"existing":[23],"CDR":[24,60],"models":[25],"assume":[26],"that":[27,75,199],"both":[28,52,83,152],"source":[30,54],"and":[31,55,86,154,173,195],"target":[32,56],"domains":[33,57],"share":[34],"same":[36],"overlapped":[37,85],"user":[38,161,171],"set":[39],"for":[40,142,158],"transfer.":[42],"However,":[43],"only":[44],"few":[45],"proportion":[46],"users":[48,88,104,116,184],"simultaneously":[49],"activate":[50],"on":[51,67,193],"practical":[59],"tasks.":[61],"In":[62],"this":[63,124],"paper,":[64],"we":[65,126],"focus":[66],"Partially":[69],"Overlapped":[70],"(POCDR)":[73],"problem,":[74,145],"is,":[76],"how":[77],"leverage":[79],"information":[81],"non-overlapped":[87,103],"improve":[90],"recommendation":[91,140],"performance.":[92],"Existing":[93],"approaches":[94],"cannot":[95],"fully":[96],"useful":[99],"behind":[101],"across":[105],"domains,":[106],"which":[107,146],"limits":[108],"model":[110],"performance":[111],"when":[112],"majority":[114],"turn":[117],"out":[118],"be":[120],"non-overlapped.":[121],"To":[122],"address":[123],"issue,":[125],"propose":[127],"an":[128],"end-to-end":[129],"Dual-autoencoder":[130],"with":[131,151,185],"Variational":[132],"Domain-invariant":[133],"Embedding":[134],"Alignment":[135],"(VDEA)":[136],"model,":[137],"a":[138],"cross-domain":[139],"framework":[141],"POCDR":[144,209],"utilizes":[147,175],"dual":[148],"variational":[149,166],"autoencoders":[150],"local":[153],"global":[155],"embedding":[156],"alignment":[157],"exploiting":[159],"domain-invariant":[160],"embedding.":[162],"VDEA":[163,200],"first":[164],"adopts":[165],"inference":[167],"capture":[169],"collaborative":[170],"preferences,":[172],"then":[174],"Gromov-Wasserstein":[176],"distribution":[177],"co-clustering":[178],"optimal":[179],"transport":[180],"cluster":[182],"similar":[186],"rating":[187],"interaction":[188],"behaviors.":[189],"Our":[190],"empirical":[191],"studies":[192],"Douban":[194],"Amazon":[196],"datasets":[197],"demonstrate":[198],"significantly":[201],"outperforms":[202],"state-of-the-art":[204],"models,":[205],"especially":[206],"under":[207],"setting.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}
