{"id":"https://openalex.org/W4407953575","doi":"https://doi.org/10.1145/3701551.3703570","title":"UIPN: User Intent Profiling Network for Multi Behavior Modeling in CTR Prediction","display_name":"UIPN: User Intent Profiling Network for Multi Behavior Modeling in CTR Prediction","publication_year":2025,"publication_date":"2025-02-26","ids":{"openalex":"https://openalex.org/W4407953575","doi":"https://doi.org/10.1145/3701551.3703570"},"language":"en","primary_location":{"id":"doi:10.1145/3701551.3703570","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703570","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701551.3703570?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701551.3703570?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115596405","display_name":"Yang Xu","orcid":"https://orcid.org/0009-0000-9798-0903"},"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":"Xu Yang","raw_affiliation_strings":["Tencent Inc., Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116425599","display_name":"Guangyuan Yu","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":"Guangyuan Yu","raw_affiliation_strings":["Tencent Inc., Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5116425600","display_name":"Jun He","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":"Jun He","raw_affiliation_strings":["Tencent Inc., Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5115596405"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03952137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"847","last_page":"856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9976999759674072,"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":0.9976999759674072,"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":0.9894999861717224,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9625999927520752,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/profiling","display_name":"Profiling (computer programming)","score":0.8181259632110596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6785795092582703},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.111409991979599}],"concepts":[{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.8181259632110596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6785795092582703},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.111409991979599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3701551.3703570","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703570","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701551.3703570?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3701551.3703570","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701551.3703570","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701551.3703570?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4407953575.pdf","grobid_xml":"https://content.openalex.org/works/W4407953575.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1985759455","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2090883204","https://openalex.org/W2143570267","https://openalex.org/W2151153134","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2512971201","https://openalex.org/W2517540742","https://openalex.org/W2548570154","https://openalex.org/W2596356468","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2788490371","https://openalex.org/W2793768763","https://openalex.org/W2898085636","https://openalex.org/W2942947041","https://openalex.org/W2945772520","https://openalex.org/W2951045934","https://openalex.org/W2951369132","https://openalex.org/W2962745591","https://openalex.org/W2963323306","https://openalex.org/W2963367478","https://openalex.org/W2963895309","https://openalex.org/W2964052347","https://openalex.org/W2984100107","https://openalex.org/W2994850640","https://openalex.org/W2998207486","https://openalex.org/W3022150987","https://openalex.org/W3032044946","https://openalex.org/W3035669589","https://openalex.org/W3035717151","https://openalex.org/W3093519337","https://openalex.org/W3096591391","https://openalex.org/W3098723082","https://openalex.org/W3100199015","https://openalex.org/W3101704389","https://openalex.org/W3104030692","https://openalex.org/W3104669598","https://openalex.org/W3105595718","https://openalex.org/W3106252282","https://openalex.org/W3117684406","https://openalex.org/W3132126111","https://openalex.org/W3167730891","https://openalex.org/W4224952158","https://openalex.org/W4281842213","https://openalex.org/W4284668205","https://openalex.org/W4285428788"],"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":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3],"models":[4,65],"often":[5],"depict":[6],"a":[7,11,107],"user's":[8],"interest":[9],"as":[10,25,54],"fixed-length":[12],"vector":[13],"derived":[14],"from":[15,88],"her":[16],"historical":[17],"behaviors,":[18],"encompassing":[19],"various":[20,133],"types":[21],"of":[22,123,145],"actions":[23],"such":[24],"clicks,":[26],"likes,":[27],"and":[28,83,126],"purchases.":[29],"Recently,":[30],"several":[31],"approaches":[32],"have":[33],"been":[34,157],"developed":[35],"to":[36,48,94,99],"capture":[37],"users'":[38,132,146],"multiple":[39,118],"interests.":[40],"For":[41],"accurate":[42],"multi-behavior":[43,64],"prediction,":[44],"it":[45],"is":[46,121],"essential":[47],"represent":[49],"complex":[50],"behavior":[51,61,91],"dependencies":[52,56],"effectively,":[53],"these":[55],"are":[57],"manifested":[58],"through":[59],"different":[60,80],"types.":[62],"Advanced":[63],"learn":[66],"relationships":[67],"among":[68],"behaviors":[69,77,134],"based":[70],"on":[71,162],"all":[72],"previous":[73],"interactions.":[74],"However,":[75],"diverse":[76],"may":[78],"indicate":[79],"user":[81,136],"intentions":[82],"unrelated":[84],"interactions":[85,147],"can":[86,141],"distract":[87],"the":[89,101,149],"target":[90],"that":[92],"needs":[93],"be":[95],"predicted.":[96],"In":[97],"order":[98],"address":[100],"limitations":[102],"highlighted":[103],"before,":[104],"we":[105],"propose":[106],"new":[108],"approach":[109,155],"called":[110],"User":[111],"Intent":[112],"Profiling":[113],"Network":[114],"(UIPN)":[115],"for":[116,131],"modeling":[117],"behaviors.":[119],"UIPN":[120],"capable":[122],"learning":[124],"behavior-specific":[125],"behavior-dependent":[127],"intention":[128],"embedding":[129],"vectors":[130],"using":[135],"intent":[137],"extractors.":[138],"These":[139],"extractors":[140],"provide":[142],"explicit":[143],"explanations":[144],"in":[148],"online":[150],"advertising":[151],"system.":[152],"The":[153],"proposed":[154],"has":[156],"validated":[158],"by":[159],"extensive":[160],"experiments":[161],"public":[163],"datasets,":[164],"which":[165],"illustrate":[166],"its":[167],"effectiveness.":[168]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
