{"id":"https://openalex.org/W3217015614","doi":"https://doi.org/10.1145/3534678.3539125","title":"Contrastive Cross-domain Recommendation in Matching","display_name":"Contrastive Cross-domain Recommendation in Matching","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W3217015614","doi":"https://doi.org/10.1145/3534678.3539125","mag":"3217015614"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539125","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539125","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-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/A5101577090","display_name":"Ruobing Xie","orcid":"https://orcid.org/0000-0003-3170-5647"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruobing Xie","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115061716","display_name":"Qi Liu","orcid":"https://orcid.org/0000-0002-4953-1537"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Liu","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017706182","display_name":"Liangdong Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangdong Wang","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035078709","display_name":"Shukai Liu","orcid":"https://orcid.org/0000-0001-7369-2958"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shukai Liu","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335384","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0003-2942-1311"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023086553","display_name":"Leyu Lin","orcid":"https://orcid.org/0000-0001-5471-500X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leyu Lin","raw_affiliation_strings":["WeChat, Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeChat, Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101577090"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":13.154,"has_fulltext":false,"cited_by_count":94,"citation_normalized_percentile":{"value":0.99099099,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4226","last_page":"4236"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9743000268936157,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9710000157356262,"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/computer-science","display_name":"Computer science","score":0.8620846271514893},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6679751873016357},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6613235473632812},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4788065552711487},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4774894416332245},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4502342641353607},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.4462246298789978},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.42874088883399963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4218471944332123},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3745076060295105}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8620846271514893},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6679751873016357},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6613235473632812},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4788065552711487},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4774894416332245},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4502342641353607},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.4462246298789978},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.42874088883399963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4218471944332123},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3745076060295105},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539125","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539125","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1971040550","https://openalex.org/W2054141820","https://openalex.org/W2114079787","https://openalex.org/W2138621090","https://openalex.org/W2155482025","https://openalex.org/W2295739661","https://openalex.org/W2512971201","https://openalex.org/W2604662567","https://openalex.org/W2740605635","https://openalex.org/W2783666221","https://openalex.org/W2796608345","https://openalex.org/W2810397803","https://openalex.org/W2842511635","https://openalex.org/W2898085636","https://openalex.org/W2913488954","https://openalex.org/W2954808392","https://openalex.org/W2964118293","https://openalex.org/W2984100107","https://openalex.org/W2986176093","https://openalex.org/W2987219395","https://openalex.org/W2987679642","https://openalex.org/W2987999026","https://openalex.org/W2996891863","https://openalex.org/W2997130580","https://openalex.org/W2998702515","https://openalex.org/W3028156525","https://openalex.org/W3034345128","https://openalex.org/W3034978746","https://openalex.org/W3035060554","https://openalex.org/W3035313290","https://openalex.org/W3035524453","https://openalex.org/W3035637689","https://openalex.org/W3036320503","https://openalex.org/W3036446966","https://openalex.org/W3045200674","https://openalex.org/W3065542300","https://openalex.org/W3080374445","https://openalex.org/W3080642298","https://openalex.org/W3094280243","https://openalex.org/W3094390815","https://openalex.org/W3094605801","https://openalex.org/W3098400049","https://openalex.org/W3098468692","https://openalex.org/W3099026360","https://openalex.org/W3099152386","https://openalex.org/W3100260481","https://openalex.org/W3101704389","https://openalex.org/W3105441222","https://openalex.org/W3106181667","https://openalex.org/W3132008141","https://openalex.org/W3136606064","https://openalex.org/W3153325943","https://openalex.org/W3154345605","https://openalex.org/W3155450594","https://openalex.org/W3155496675","https://openalex.org/W3160558455","https://openalex.org/W3170587616","https://openalex.org/W3171249018","https://openalex.org/W3173331009","https://openalex.org/W3173365306","https://openalex.org/W3176476612","https://openalex.org/W3194486110","https://openalex.org/W3206310679","https://openalex.org/W3209185641","https://openalex.org/W4221155633","https://openalex.org/W4280595799","https://openalex.org/W6600371763"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2170801710","https://openalex.org/W2952704802","https://openalex.org/W2741781807"],"abstract_inverted_index":{"Cross-domain":[0],"recommendation":[1,7],"(CDR)":[2],"aims":[3],"to":[4,78],"provide":[5],"better":[6,101],"results":[8],"in":[9,26,31,47,68,150,172],"the":[10,14,17,32,40,116,124],"target":[11,117],"domain":[12,118],"with":[13,39],"help":[15],"of":[16,129,166],"source":[18,169],"domain,":[19],"which":[20],"is":[21,171],"widely":[22],"used":[23],"and":[24,43,51,86,93,104,112,135,147],"explored":[25],"real-world":[27,152],"systems.":[28],"However,":[29],"CDR":[30,67],"matching":[33],"(i.e.,":[34],"candidate":[35],"generation)":[36],"module":[37],"struggles":[38],"data":[41],"sparsity":[42],"popularity":[44],"bias":[45],"issues":[46],"both":[48,145],"representation":[49,102],"learning":[50,91,97,103],"knowledge":[52,105],"transfer.":[53,106],"In":[54,138],"this":[55],"work,":[56],"we":[57,71,155],"propose":[58],"a":[59,73,120,151],"novel":[60],"Contrastive":[61],"Cross-Domain":[62],"Recommendation":[63],"(CCDR)":[64],"framework":[65],"for":[66,100],"matching.":[69],"Specifically,":[70],"build":[72],"huge":[74],"diversified":[75],"preference":[76],"network":[77],"capture":[79],"multiple":[80],"information":[81],"reflecting":[82],"user":[83],"diverse":[84],"interests,":[85],"design":[87],"an":[88],"intra-domain":[89],"contrastive":[90,96],"(intra-CL)":[92],"three":[94],"inter-domain":[95],"(inter-CL)":[98],"tasks":[99],"The":[107,168],"intra-CL":[108],"enables":[109],"more":[110],"effective":[111],"balanced":[113],"training":[114],"inside":[115],"via":[119],"graph":[121],"augmentation,":[122],"while":[123],"inter-CL":[125],"builds":[126],"different":[127],"types":[128],"cross-domain":[130],"interactions":[131],"from":[132],"user,":[133],"taxonomy,":[134],"neighbor":[136],"aspects.":[137],"experiments,":[139],"CCDR":[140,159],"achieves":[141],"significant":[142],"improvements":[143],"on":[144,160],"offline":[146],"online":[148],"evaluations":[149],"system.":[153],"Currently,":[154],"have":[156],"deployed":[157],"our":[158],"WeChat":[161],"Top":[162],"Stories,":[163],"affecting":[164],"plenty":[165],"users.":[167],"code":[170],"https://github.com/lqfarmer/CCDR.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":4}],"updated_date":"2026-03-26T15:22:09.906841","created_date":"2025-10-10T00:00:00"}