{"id":"https://openalex.org/W3155455841","doi":"https://doi.org/10.1145/3404835.3463117","title":"Deep Position-wise Interaction Network for CTR Prediction","display_name":"Deep Position-wise Interaction Network for CTR Prediction","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3155455841","doi":"https://doi.org/10.1145/3404835.3463117","mag":"3155455841"},"language":"en","primary_location":{"id":"doi:10.1145/3404835.3463117","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404835.3463117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2106.05482","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079865276","display_name":"Jianqiang Huang","orcid":"https://orcid.org/0000-0001-5735-2910"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianqiang Huang","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029338576","display_name":"Ke Hu","orcid":"https://orcid.org/0000-0002-1599-1519"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Hu","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059534557","display_name":"Qingtao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingtao Tang","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101492066","display_name":"Mingjian Chen","orcid":"https://orcid.org/0009-0004-9365-8684"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjian Chen","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025047919","display_name":"Yi Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Qi","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003039093","display_name":"Jia Cheng","orcid":"https://orcid.org/0000-0003-1702-4263"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Cheng","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084801659","display_name":"Jun Lei","orcid":"https://orcid.org/0000-0002-4015-8668"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Lei","raw_affiliation_strings":["Meituan, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5079865276"],"corresponding_institution_ids":["https://openalex.org/I4210087373"],"apc_list":null,"apc_paid":null,"fwci":4.8207,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.95185404,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1885","last_page":"1889"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.7946916818618774},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7307612299919128},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7201524972915649},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6964113712310791},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6082730293273926},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.5726632475852966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4986226558685303},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43774956464767456},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.43336474895477295},{"id":"https://openalex.org/keywords/position-paper","display_name":"Position paper","score":0.4201841354370117},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4120723605155945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3314182758331299},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.29581722617149353},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.0800606906414032}],"concepts":[{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.7946916818618774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7307612299919128},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7201524972915649},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6964113712310791},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6082730293273926},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.5726632475852966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4986226558685303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43774956464767456},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.43336474895477295},{"id":"https://openalex.org/C78780964","wikidata":"https://www.wikidata.org/wiki/Q7233193","display_name":"Position paper","level":2,"score":0.4201841354370117},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4120723605155945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3314182758331299},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29581722617149353},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0800606906414032},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3404835.3463117","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404835.3463117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2106.05482","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.05482","pdf_url":"https://arxiv.org/pdf/2106.05482","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2106.05482","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.05482","pdf_url":"https://arxiv.org/pdf/2106.05482","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2340526403","https://openalex.org/W2475334473","https://openalex.org/W2507134384","https://openalex.org/W2610314927","https://openalex.org/W2626778328","https://openalex.org/W2723293840","https://openalex.org/W2769473018","https://openalex.org/W2797359146","https://openalex.org/W2797400361","https://openalex.org/W2890044493","https://openalex.org/W2905569957","https://openalex.org/W2912255075","https://openalex.org/W2945944361","https://openalex.org/W2951001079","https://openalex.org/W2963189767","https://openalex.org/W2964182926","https://openalex.org/W2972358762","https://openalex.org/W2973172293","https://openalex.org/W3012600133","https://openalex.org/W3035404611","https://openalex.org/W3035716173","https://openalex.org/W3080768030","https://openalex.org/W3088393583","https://openalex.org/W3092103025","https://openalex.org/W3101148092","https://openalex.org/W3102540985","https://openalex.org/W3105712174","https://openalex.org/W6602120790"],"related_works":["https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2798198862","https://openalex.org/W2915512527","https://openalex.org/W2055243143","https://openalex.org/W51364034","https://openalex.org/W1546381263","https://openalex.org/W4387848858","https://openalex.org/W4385573609","https://openalex.org/W3171064680"],"abstract_inverted_index":{"Click-through":[0],"rate(CTR)":[1],"prediction":[2],"plays":[3],"an":[4],"important":[5],"role":[6],"in":[7],"online":[8,84],"advertising":[9],"and":[10,50,75,82,103,108,117],"recommender":[11],"systems.":[12],"In":[13],"practice,":[14],"the":[15,60,67,87,94,98,112],"training":[16,45,54,74],"of":[17,70,90,100],"CTR":[18,36],"models":[19],"depends":[20],"on":[21],"click":[22,95],"data":[23],"which":[24],"is":[25,97,106],"intrinsically":[26],"biased":[27],"towards":[28],"higher":[29,32,35],"positions":[30],"since":[31],"position":[33,44,48,57,71,116],"has":[34],"by":[37],"nature.":[38],"Existing":[39],"methods":[40],"such":[41],"as":[42],"actual":[43],"with":[46,55],"fixed":[47],"inference":[49,58,76],"inverse":[51],"propensity":[52],"weighted":[53],"no":[56],"alleviate":[59],"bias":[61],"problem":[62],"to":[63,80,110],"some":[64],"extend.":[65],"However,":[66],"different":[68],"treatment":[69],"information":[72],"between":[73,115],"will":[77],"inevitably":[78],"lead":[79],"inconsistency":[81],"sub-optimal":[83],"performance.":[85],"Meanwhile,":[86],"basic":[88],"assumption":[89],"these":[91],"methods,":[92],"i.e.,":[93],"probability":[96,102],"product":[99],"examination":[101],"relevance":[104],"probability,":[105],"oversimplified":[107],"insufficient":[109],"model":[111],"rich":[113],"interaction":[114],"other":[118],"information.":[119]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
