{"id":"https://openalex.org/W4392384821","doi":"https://doi.org/10.1145/3616855.3635808","title":"AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations","display_name":"AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations","publication_year":2024,"publication_date":"2024-03-04","ids":{"openalex":"https://openalex.org/W4392384821","doi":"https://doi.org/10.1145/3616855.3635808"},"language":"en","primary_location":{"id":"doi:10.1145/3616855.3635808","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616855.3635808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5101990149","display_name":"He Wei","orcid":"https://orcid.org/0000-0002-1248-1804"},"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":"He Wei","raw_affiliation_strings":["Machine learning platform department, Tencent TEG, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030381144","display_name":"Yuekui Yang","orcid":"https://orcid.org/0000-0002-8709-3128"},"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"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuekui Yang","raw_affiliation_strings":["Machine learning platform department, Tencent TEG & Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG & Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I2250653659","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760812","display_name":"Shaoping Ma","orcid":"https://orcid.org/0000-0002-8762-8268"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoping Ma","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081899382","display_name":"Haiyang Wu","orcid":"https://orcid.org/0000-0001-7314-7618"},"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":"Haiyang Wu","raw_affiliation_strings":["Machine learning platform department, Tencent TEG, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071091858","display_name":"Yangyang Tang","orcid":"https://orcid.org/0000-0002-6563-6734"},"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":"Yangyang Tang","raw_affiliation_strings":["Machine learning platform department, Tencent TEG, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079004512","display_name":"Meixi Liu","orcid":"https://orcid.org/0000-0001-7488-8143"},"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":"Meixi Liu","raw_affiliation_strings":["Machine learning platform department, Tencent TEG, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070213703","display_name":"Yunji Zhang","orcid":"https://orcid.org/0009-0000-0698-8361"},"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":"Yang Zhang","raw_affiliation_strings":["Machine learning platform department, Tencent TEG, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machine learning platform department, Tencent TEG, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101990149"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02682338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"797","last_page":"805"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9943000078201294,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9908999800682068,"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/pooling","display_name":"Pooling","score":0.9379291534423828},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7067430019378662},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.6102248430252075},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6092676520347595},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.6074948906898499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5268755555152893},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5221248269081116},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5207617282867432},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4507862329483032},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4377361834049225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3902777135372162},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29392313957214355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1848756968975067}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.9379291534423828},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067430019378662},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.6102248430252075},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6092676520347595},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.6074948906898499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5268755555152893},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5221248269081116},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5207617282867432},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4507862329483032},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4377361834049225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3902777135372162},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29392313957214355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1848756968975067},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3616855.3635808","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616855.3635808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3379573397","display_name":null,"funder_award_id":"U21B2026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6166362743","display_name":null,"funder_award_id":"U21B2026","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W10548402","https://openalex.org/W1836465849","https://openalex.org/W1840106123","https://openalex.org/W2154579312","https://openalex.org/W2296073425","https://openalex.org/W2422675628","https://openalex.org/W2475334473","https://openalex.org/W2512971201","https://openalex.org/W2548570154","https://openalex.org/W2723293840","https://openalex.org/W2739273093","https://openalex.org/W2793768763","https://openalex.org/W2799258971","https://openalex.org/W2804268694","https://openalex.org/W2886851211","https://openalex.org/W2910628332","https://openalex.org/W2962746461","https://openalex.org/W2963323306","https://openalex.org/W2985793077","https://openalex.org/W3008650695","https://openalex.org/W3023259715","https://openalex.org/W3034483718","https://openalex.org/W3081190557","https://openalex.org/W3081362488","https://openalex.org/W3093965394","https://openalex.org/W3104030692","https://openalex.org/W3154430477","https://openalex.org/W3187174779","https://openalex.org/W4236965008","https://openalex.org/W4242926951","https://openalex.org/W4367046992","https://openalex.org/W6676231525","https://openalex.org/W6730252483"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Large-scale":[0],"recommender":[1],"systems":[2],"usually":[3],"contain":[4],"hundreds":[5],"of":[6,14],"multi-valued":[7,35],"feature":[8,36,60,109],"fields,":[9,51],"which":[10,82],"have":[11,62],"different":[12,59,63,69,174],"number":[13],"values":[15],"in":[16,22,107,168],"each":[17,94,108],"field.":[18,110],"For":[19],"easier":[20],"computation":[21],"traditional":[23],"fixed-shape":[24],"neural":[25],"networks,":[26],"pooling":[27,47,70,91,105,129],"operators":[28,106],"are":[29,100],"widely":[30],"used":[31,116],"to":[32,55,102,117],"compress":[33],"the":[34,89,123,127,136,141,157],"into":[37],"a":[38,45],"fixed-dimension":[39],"vector.":[40],"Most":[41],"existing":[42],"works":[43],"set":[44],"single":[46],"method":[48,165],"for":[49,88,93],"all":[50,103],"but":[52],"this":[53,73],"leads":[54],"sub-optimal":[56],"results,":[57],"because":[58],"fields":[61],"information":[64],"distributions":[65],"and":[66,85,122,147,173],"thus":[67],"require":[68],"methods.":[71],"In":[72],"work,":[74],"we":[75],"propose":[76],"an":[77,112],"AutoML-based":[78,113],"framework,":[79],"called":[80],"AutoPooling,":[81],"can":[83,131],"automatically":[84],"efficiently":[86],"search":[87,175],"optimal":[90,128],"operator":[92,130],"multivalued":[95],"feature.":[96],"Specifically,":[97],"learnable":[98],"weights":[99],"assigned":[101],"candidate":[104],"Then":[111],"algorithm":[114],"is":[115,166],"learn":[118],"both":[119,145],"model":[120],"parameters":[121],"field-aware":[124],"weights.":[125,138],"Finally,":[126],"be":[132],"acquired":[133],"based":[134],"on":[135,144],"associated":[137],"We":[139],"evaluate":[140],"proposed":[142],"framework":[143],"public":[146],"industrial":[148],"datasets.":[149],"The":[150],"results":[151,161],"show":[152,162],"that":[153,163],"AutoPooling":[154],"significantly":[155],"outperforms":[156],"benchmarks.":[158],"Further":[159],"experiment":[160],"our":[164],"robust":[167],"various":[169],"deep":[170],"recommend":[171],"models":[172],"spaces.":[176]},"counts_by_year":[],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}