{"id":"https://openalex.org/W3153252032","doi":"https://doi.org/10.1145/3442381.3450125","title":"UserSim: User Simulation via Supervised GenerativeAdversarial Network","display_name":"UserSim: User Simulation via Supervised GenerativeAdversarial Network","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3153252032","doi":"https://doi.org/10.1145/3442381.3450125","mag":"3153252032"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3450125","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450125","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 Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3450125","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100645854","display_name":"Xiangyu Zhao","orcid":"https://orcid.org/0000-0003-2926-4416"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]},{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK","US"],"is_corresponding":true,"raw_author_name":"Xiangyu Zhao","raw_affiliation_strings":["Michigan State University and City University of Hong Kong, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University and City University of Hong Kong, USA","institution_ids":["https://openalex.org/I168719708","https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103251082","display_name":"Long Xia","orcid":"https://orcid.org/0000-0003-2580-6206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long Xia","raw_affiliation_strings":["York University, China"],"affiliations":[{"raw_affiliation_string":"York University, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089307887","display_name":"Lixin Zou","orcid":"https://orcid.org/0000-0001-6755-871X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixin Zou","raw_affiliation_strings":["Baidu, China"],"affiliations":[{"raw_affiliation_string":"Baidu, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032165975","display_name":"Hui Liu","orcid":"https://orcid.org/0000-0002-6823-0756"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Liu","raw_affiliation_strings":["Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054482111","display_name":"Dawei Yin","orcid":"https://orcid.org/0000-0002-8846-2001"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dawei Yin","raw_affiliation_strings":["Baidu, USA"],"affiliations":[{"raw_affiliation_string":"Baidu, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040639891","display_name":"Jiliang Tang","orcid":"https://orcid.org/0000-0001-7125-3898"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiliang Tang","raw_affiliation_strings":["Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100645854"],"corresponding_institution_ids":["https://openalex.org/I168719708","https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":1.441,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.83697712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3582","last_page":"3589"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9951000213623047,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9951000213623047,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9926999807357788,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9915000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7992318868637085},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.4187089800834656},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3331366181373596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7992318868637085},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4187089800834656},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3331366181373596}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3450125","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450125","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 Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3450125","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450125","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 Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W46659105","https://openalex.org/W1720514416","https://openalex.org/W1980969546","https://openalex.org/W1986760411","https://openalex.org/W2054436222","https://openalex.org/W2099471712","https://openalex.org/W2105256943","https://openalex.org/W2120100126","https://openalex.org/W2121863487","https://openalex.org/W2154543878","https://openalex.org/W2161395589","https://openalex.org/W2186512312","https://openalex.org/W2215378786","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2583674722","https://openalex.org/W2619206542","https://openalex.org/W2784068709","https://openalex.org/W2787933113","https://openalex.org/W2788295351","https://openalex.org/W2799544270","https://openalex.org/W2808980893","https://openalex.org/W2892888989","https://openalex.org/W2902572901","https://openalex.org/W2941385591","https://openalex.org/W2951431594","https://openalex.org/W2963440040","https://openalex.org/W2963654596","https://openalex.org/W2963825768","https://openalex.org/W2963842088","https://openalex.org/W2964157711","https://openalex.org/W2965512832","https://openalex.org/W2965871673","https://openalex.org/W2971998163","https://openalex.org/W2972561734","https://openalex.org/W2984869362","https://openalex.org/W2996959725","https://openalex.org/W3002944878","https://openalex.org/W3007094061","https://openalex.org/W3040127368","https://openalex.org/W3043826557","https://openalex.org/W3096739060","https://openalex.org/W3099420497","https://openalex.org/W3101023724","https://openalex.org/W3102778384","https://openalex.org/W3102899483","https://openalex.org/W3103141630","https://openalex.org/W3104966867","https://openalex.org/W3105787366","https://openalex.org/W3123956618","https://openalex.org/W3175142666"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"With":[0],"the":[1,36,81,114,117,138,160,163],"recent":[2],"advances":[3],"in":[4,13,35,72],"Reinforcement":[5],"Learning":[6],"(RL),":[7],"there":[8],"have":[9],"been":[10],"tremendous":[11],"interests":[12],"employing":[14],"RL":[15],"for":[16,52,91],"recommender":[17],"systems.":[18],"However,":[19],"directly":[20],"training":[21],"and":[22,43,64,87,124,144],"evaluating":[23],"a":[24,53,73,101,106],"new":[25,66],"RL-based":[26],"recommendation":[27,67],"algorithm":[28],"needs":[29],"to":[30,62],"collect":[31],"users\u2019":[32,47,60,70,121,150],"real-time":[33],"feedback":[34],"real":[37,59,135,143],"system,":[38],"which":[39],"is":[40,85],"time/effort":[41],"consuming":[42],"could":[44],"negatively":[45],"impact":[46],"experiences.":[48],"Thus,":[49],"it":[50],"calls":[51],"user":[54,93,102],"simulator":[55,103],"that":[56,128],"can":[57,129],"mimic":[58],"behaviors":[61,71],"pre-train":[63],"evaluate":[65],"algorithms.":[68],"Simulating":[69],"dynamic":[74],"system":[75],"faces":[76],"immense":[77],"challenges":[78],"\u2013":[79],"(i)":[80],"underlying":[82,118],"item":[83],"distribution":[84,119],"complex,":[86],"(ii)":[88],"historical":[89,122],"logs":[90,123,127,146],"each":[92],"are":[94],"limited.":[95],"In":[96],"this":[97],"paper,":[98],"we":[99],"develop":[100],"based":[104,155],"on":[105,156],"Generative":[107],"Adversarial":[108],"Network":[109],"(GAN).":[110],"To":[111],"be":[112,130],"specific,":[113],"generator":[115],"captures":[116],"of":[120,134,162],"generates":[125],"realistic":[126],"considered":[131],"as":[132],"augmentations":[133],"logs;":[136],"while":[137],"discriminator":[139],"not":[140],"only":[141],"distinguishes":[142],"fake":[145],"but":[147],"also":[148],"predicts":[149],"behaviors.":[151],"The":[152],"experimental":[153],"results":[154],"benchmark":[157],"datasets":[158],"demonstrate":[159],"effectiveness":[161],"proposed":[164],"simulator.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}