{"id":"https://openalex.org/W4224989601","doi":"https://doi.org/10.1145/3477495.3531788","title":"Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction","display_name":"Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4224989601","doi":"https://doi.org/10.1145/3477495.3531788"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531788","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531788","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/A5100328758","display_name":"Xiaochen Li","orcid":"https://orcid.org/0000-0002-5068-1938"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochen Li","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112582835","display_name":"Jian Liang","orcid":"https://orcid.org/0000-0003-3890-1894"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Liang","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040500162","display_name":"Xialong Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xialong Liu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100433703","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0003-1632-167X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Lazada Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lazada Group, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7587,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.86129781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1969","last_page":"1973"},"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9970999956130981,"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/T11478","display_name":"Caching and Content Delivery","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8208186626434326},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.652825653553009},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5961868166923523},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5487797260284424},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.5123802423477173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49754026532173157},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.49337664246559143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4873448312282562},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4580186605453491},{"id":"https://openalex.org/keywords/user-modeling","display_name":"User modeling","score":0.4268733263015747},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4148840308189392},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.24197903275489807},{"id":"https://openalex.org/keywords/user-interface","display_name":"User interface","score":0.1599811613559723}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8208186626434326},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.652825653553009},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5961868166923523},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5487797260284424},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.5123802423477173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49754026532173157},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.49337664246559143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4873448312282562},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4580186605453491},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.4268733263015747},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4148840308189392},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.24197903275489807},{"id":"https://openalex.org/C89505385","wikidata":"https://www.wikidata.org/wiki/Q47146","display_name":"User interface","level":2,"score":0.1599811613559723},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3531788","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531788","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2604202871","https://openalex.org/W2723293840","https://openalex.org/W2783666221","https://openalex.org/W2896457183","https://openalex.org/W2942947041","https://openalex.org/W2945772520","https://openalex.org/W2962745591","https://openalex.org/W2971196067","https://openalex.org/W2984100107","https://openalex.org/W3032044946","https://openalex.org/W3035625410","https://openalex.org/W3080642298","https://openalex.org/W3093519337","https://openalex.org/W3100199015","https://openalex.org/W3101681922","https://openalex.org/W3102937163","https://openalex.org/W3105595718","https://openalex.org/W3106181667","https://openalex.org/W3106252282","https://openalex.org/W3190524507"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W3093519337","https://openalex.org/W3153996400"],"abstract_inverted_index":{"Rich":[0],"user":[1,12,58,76,103,131],"behavior":[2,53,114,122],"information":[3,73,123],"is":[4],"of":[5,38,83],"great":[6],"importance":[7],"for":[8,139],"capturing":[9],"and":[10,32,42,55,116,147],"understanding":[11],"interest":[13,136],"in":[14,30,74],"click-through":[15],"rate":[16],"(CTR)":[17],"prediction.":[18,141],"To":[19,87],"improve":[20],"the":[21,36,71,81],"richness,":[22],"collecting":[23],"long-term":[24,52,75,102],"behaviors":[25,132],"becomes":[26],"a":[27,94,107],"typical":[28],"approach":[29],"academy":[31],"industry":[33],"but":[34,66],"at":[35],"cost":[37,64],"increasing":[39],"online":[40,63],"storage":[41],"latency.":[43],"Recently,":[44],"researchers":[45],"have":[46],"proposed":[47],"several":[48],"approaches":[49,61],"to":[50,100,111,119],"shorten":[51],"sequence":[54,115],"then":[56,117],"model":[57,101],"interests.":[59],"These":[60],"reduce":[62],"efficiently":[65],"do":[67],"not":[68],"well":[69],"handle":[70],"noisy":[72],"behavior,":[77],"which":[78],"may":[79],"deteriorate":[80],"performance":[82],"CTR":[84,140],"prediction":[85],"significantly.":[86],"obtain":[88],"better":[89],"cost/performance":[90],"trade-off,":[91],"we":[92],"propose":[93],"novel":[95],"Adversarial":[96],"Filtering":[97],"Model":[98],"(ADFM)":[99],"behavior.":[104],"ADFM":[105],"uses":[106],"hierarchical":[108],"aggregation":[109],"representation":[110],"compress":[112],"raw":[113],"learns":[118],"remove":[120],"useless":[121],"with":[124],"an":[125],"adversarial":[126],"filtering":[127],"mechanism.":[128],"The":[129],"selected":[130],"are":[133],"fed":[134],"into":[135],"extraction":[137],"module":[138],"Experimental":[142],"results":[143],"on":[144],"public":[145],"datasets":[146],"industrial":[148],"dataset":[149],"demonstrate":[150],"that":[151],"our":[152],"method":[153],"achieves":[154],"significant":[155],"improvements":[156],"over":[157],"state-of-the-art":[158],"models.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
