{"id":"https://openalex.org/W4407953145","doi":"https://doi.org/10.1145/3701551.3703557","title":"Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models","display_name":"Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models","publication_year":2025,"publication_date":"2025-02-26","ids":{"openalex":"https://openalex.org/W4407953145","doi":"https://doi.org/10.1145/3701551.3703557"},"language":"en","primary_location":{"id":"doi:10.1145/3701551.3703557","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3701551.3703557","pdf_url":null,"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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051754850","display_name":"Kexin Zhang","orcid":"https://orcid.org/0000-0003-2678-8556"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kexin Zhang","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079054140","display_name":"Fuyuan Lyu","orcid":"https://orcid.org/0000-0001-9345-1828"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fuyuan Lyu","raw_affiliation_strings":["McGill University, Montreal, QC, Canada, &amp; MILA, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, QC, Canada, &amp; MILA, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071989106","display_name":"Xing Tang","orcid":"https://orcid.org/0000-0003-4360-0754"},"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":"Xing Tang","raw_affiliation_strings":["FiT, Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"FiT, Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003106644","display_name":"Dugang Liu","orcid":"https://orcid.org/0000-0003-3612-709X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dugang Liu","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410908","display_name":"Chen Ma","orcid":"https://orcid.org/0000-0001-7933-9813"},"institutions":[{"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"],"is_corresponding":false,"raw_author_name":"Chen Ma","raw_affiliation_strings":["City University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044455276","display_name":"Kaize Ding","orcid":"https://orcid.org/0000-0001-6684-6752"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaize Ding","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083350101","display_name":"Xiuqiang He","orcid":"https://orcid.org/0000-0002-4115-8205"},"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":"Xiuqiang He","raw_affiliation_strings":["FiT, Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"FiT, Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100372152","display_name":"Xue Liu","orcid":"https://orcid.org/0000-0001-5252-3442"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xue Liu","raw_affiliation_strings":["McGill University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5051754850"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":2.7246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.87798688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"744","last_page":"753"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9878000020980835,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9878000020980835,"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"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9851999878883362,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9732999801635742,"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/fusion","display_name":"Fusion","score":0.7117199897766113},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6298243999481201},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5130121111869812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47896602749824524},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.45808732509613037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34378352761268616},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.13811573386192322},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.05738416314125061}],"concepts":[{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.7117199897766113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6298243999481201},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5130121111869812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47896602749824524},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.45808732509613037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34378352761268616},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.13811573386192322},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.05738416314125061},{"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:10.1145/3701551.3703557","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3701551.3703557","pdf_url":null,"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":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1985759455","https://openalex.org/W2090883204","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2548570154","https://openalex.org/W2604662567","https://openalex.org/W2793768763","https://openalex.org/W2898085636","https://openalex.org/W2911760887","https://openalex.org/W2946044191","https://openalex.org/W2954817175","https://openalex.org/W2964052347","https://openalex.org/W2964182926","https://openalex.org/W2967733054","https://openalex.org/W3041360407","https://openalex.org/W3081190557","https://openalex.org/W3081362488","https://openalex.org/W3093945404","https://openalex.org/W3093965394","https://openalex.org/W3098024612","https://openalex.org/W3101704389","https://openalex.org/W3104030692","https://openalex.org/W3104506006","https://openalex.org/W3104789011","https://openalex.org/W3153687269","https://openalex.org/W3155651553","https://openalex.org/W3192708477","https://openalex.org/W3208709726","https://openalex.org/W4224307215","https://openalex.org/W4294977709","https://openalex.org/W4320908195","https://openalex.org/W4321485437","https://openalex.org/W4367046995","https://openalex.org/W4382239960","https://openalex.org/W4384625597","https://openalex.org/W4386729838","https://openalex.org/W4392368241","https://openalex.org/W4392384765"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3046775127"],"abstract_inverted_index":{"The":[0,127],"evolution":[1],"of":[2,129,153,191],"previous":[3],"Click-Through":[4],"Rate":[5],"(CTR)":[6],"models":[7],"has":[8,29,60,112],"mainly":[9],"been":[10,30,61,87,99,114],"driven":[11],"by":[12],"proposing":[13],"complex":[14],"components,":[15],"whether":[16],"shallow":[17],"or":[18,104],"deep,":[19],"that":[20,64,78,149],"are":[21,45,177],"adept":[22],"at":[23],"modeling":[24],"feature":[25],"interactions.":[26],"However,":[27],"there":[28,85],"less":[31],"focus":[32],"on":[33,51,106],"improving":[34,194],"fusion":[35,53,57,67,79,93,120],"design.":[36],"Instead,":[37],"two":[38],"naive":[39],"solutions,":[40],"stacked":[41],"and":[42,55,137,160,189],"parallel":[43],"fusion,":[44,154],"commonly":[46],"used.":[47],"Both":[48],"solutions":[49],"rely":[50],"pre-determined":[52],"connections":[54],"fixed":[56],"operations.":[58],"It":[59],"repetitively":[62],"observed":[63],"changes":[65],"in":[66,71,81,193],"design":[68],"may":[69],"result":[70],"different":[72],"performances,":[73],"highlighting":[74],"the":[75,130,151,157,161,187],"critical":[76],"role":[77],"plays":[80],"CTR":[82,195],"models.":[83],"While":[84],"have":[86,97,165],"attempts":[88],"to":[89,101,116,135],"refine":[90],"these":[91,95,172],"basic":[92],"strategies,":[94],"efforts":[96],"often":[98],"constrained":[100],"specific":[102,107],"settings":[103],"dependent":[105],"components.":[108],"Neural":[109],"architecture":[110],"search":[111,131],"also":[113],"introduced":[115],"partially":[117],"deal":[118],"with":[119,125],"design,":[121],"but":[122],"it":[123],"comes":[124],"limitations.":[126],"complexity":[128],"space":[132],"can":[133],"lead":[134],"inefficient":[136],"ineffective":[138],"results.":[139],"To":[140],"bridge":[141],"this":[142],"gap,":[143],"we":[144],"introduce":[145],"OptFusion,":[146],"a":[147,167],"method":[148],"automates":[150],"learning":[152,159,169],"encompassing":[155],"both":[156,186],"connection":[158],"operation":[162],"selection.":[163],"We":[164],"proposed":[166],"one-shot":[168],"algorithm":[170],"tackling":[171],"tasks":[173],"concurrently.":[174],"Our":[175,198],"experiments":[176,184],"conducted":[178],"over":[179],"three":[180],"large-scale":[181],"datasets.":[182],"Extensive":[183],"prove":[185],"effectiveness":[188],"efficiency":[190],"OptFusion":[192],"model":[196],"performance.":[197],"code":[199],"implementation":[200],"is":[201],"available":[202],"here":[203],"https://github.com/kexin-kxzhang/OptFusion.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
