{"id":"https://openalex.org/W3009067351","doi":"https://doi.org/10.1109/globecom38437.2019.9013898","title":"Adversarial Learning of Transitive Semantic Features for Cross-Domain Recommendation","display_name":"Adversarial Learning of Transitive Semantic Features for Cross-Domain Recommendation","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3009067351","doi":"https://doi.org/10.1109/globecom38437.2019.9013898","mag":"3009067351"},"language":"en","primary_location":{"id":"doi:10.1109/globecom38437.2019.9013898","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013898","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","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/A5081827896","display_name":"Zhetao Li","orcid":"https://orcid.org/0000-0002-7804-0286"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhetao Li","raw_affiliation_strings":["Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China","The College of Information Engineering, Xiangtan University, Xiangtan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]},{"raw_affiliation_string":"The College of Information Engineering, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052684988","display_name":"Pengpeng Qiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengpeng Qiao","raw_affiliation_strings":["Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China","The College of Information Engineering, Xiangtan University, Xiangtan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]},{"raw_affiliation_string":"The College of Information Engineering, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020022791","display_name":"Yuanxing Zhang","orcid":"https://orcid.org/0000-0003-1460-8124"},"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":"Yuanxing Zhang","raw_affiliation_strings":["School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064314482","display_name":"Kaigui Bian","orcid":null},"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":"Kaigui Bian","raw_affiliation_strings":["School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9961000084877014,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9947999715805054,"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":0.8658170700073242},{"id":"https://openalex.org/keywords/transitive-relation","display_name":"Transitive relation","score":0.779845118522644},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.68862384557724},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6281766891479492},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5927239060401917},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.504018247127533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4969199001789093},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.46982645988464355},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.45921656489372253},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4560231864452362},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4451776146888733},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.44022437930107117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41886404156684875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8658170700073242},{"id":"https://openalex.org/C191399111","wikidata":"https://www.wikidata.org/wiki/Q64861","display_name":"Transitive relation","level":2,"score":0.779845118522644},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.68862384557724},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6281766891479492},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5927239060401917},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.504018247127533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4969199001789093},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.46982645988464355},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.45921656489372253},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4560231864452362},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4451776146888733},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.44022437930107117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41886404156684875},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom38437.2019.9013898","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013898","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W143867266","https://openalex.org/W163868085","https://openalex.org/W1502375784","https://openalex.org/W1832693441","https://openalex.org/W2040858299","https://openalex.org/W2101409192","https://openalex.org/W2118674552","https://openalex.org/W2124033848","https://openalex.org/W2127201730","https://openalex.org/W2170881581","https://openalex.org/W2524611166","https://openalex.org/W2604167613","https://openalex.org/W2752630748","https://openalex.org/W2809129535","https://openalex.org/W2958264571","https://openalex.org/W2962721744","https://openalex.org/W2963633299","https://openalex.org/W2964185501","https://openalex.org/W6605917969","https://openalex.org/W6629940606","https://openalex.org/W6678360021","https://openalex.org/W6727171001","https://openalex.org/W6736490518","https://openalex.org/W6743965040","https://openalex.org/W6750774204"],"related_works":["https://openalex.org/W2293317945","https://openalex.org/W4323349240","https://openalex.org/W1786507113","https://openalex.org/W4318960487","https://openalex.org/W1934555896","https://openalex.org/W2558028811","https://openalex.org/W2499321295","https://openalex.org/W2364400397","https://openalex.org/W3199233695","https://openalex.org/W4283836875"],"abstract_inverted_index":{"In":[0,61],"the":[1,10,30,47,77,95,98,104,110,120,139,143],"era":[2],"of":[3,13,37,56,148],"big":[4],"data,":[5],"recommender":[6],"systems":[7],"have":[8],"become":[9],"key":[11],"part":[12],"many":[14],"Internet":[15],"applications.":[16],"One":[17],"successful":[18],"recommendation":[19],"strategy":[20],"is":[21,42],"to":[22,45,75,108],"jointly":[23],"recommend":[24],"items":[25],"from":[26,58,93],"different":[27],"domains":[28,51],"where":[29],"system":[31],"can":[32,128],"model":[33],"an":[34,130],"accurate":[35,131],"portrait":[36],"user":[38,114],"behaviors.":[39],"However,":[40],"it":[41],"still":[43],"challenging":[44],"identify":[46],"correlation":[48],"among":[49,82],"various":[50,83],"and":[52,97,101,151],"make":[53],"efficient":[54],"utilization":[55],"features":[57,81,92],"each":[59],"domain.":[60],"this":[62],"paper,":[63],"we":[64],"propose":[65],"a":[66],"novel":[67],"framework,":[68],"called":[69],"Domain":[70],"Adversarial":[71],"Cross-Domain":[72],"Recommendation":[73],"(DACDR),":[74],"learn":[76],"implicit":[78],"transitive":[79,105],"semantic":[80,91],"information":[84],"relevant":[85],"domains.":[86,112],"The":[87,113],"framework":[88,141],"automatically":[89],"retrieves":[90],"both":[94],"source":[96],"target":[99],"domains,":[100],"adaptively":[102],"learns":[103],"latent":[106,122],"factors":[107],"connect":[109],"two":[111],"behaviors":[115],"are":[116],"then":[117],"modelled":[118],"by":[119],"learnt":[121],"factors,":[123],"based":[124],"on":[125],"which":[126],"DACDR":[127],"provide":[129],"recommendation.":[132],"Evaluation":[133],"over":[134],"real-world":[135],"dataset":[136],"verifies":[137],"that":[138],"proposed":[140],"outperforms":[142],"state-of-the-art":[144],"algorithms":[145],"in":[146],"terms":[147],"F1,":[149],"NDCG":[150],"MRR":[152],"metrics.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
