{"id":"https://openalex.org/W4416798564","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249407","title":"Incorporating Semantic Visual Content into Click-Through Rate Prediction for Video Advertisements","display_name":"Incorporating Semantic Visual Content into Click-Through Rate Prediction for Video Advertisements","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4416798564","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249407"},"language":null,"primary_location":{"id":"doi:10.1109/apsipaasc65261.2025.11249407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5042344549","display_name":"Yoshiaki Tanabe","orcid":"https://orcid.org/0000-0001-5858-7834"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yoshiaki Tanabe","raw_affiliation_strings":["The University of Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000354904","display_name":"Shuntaro Masuda","orcid":"https://orcid.org/0009-0008-2490-9557"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuntaro Masuda","raw_affiliation_strings":["The University of Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gakumatsu Ryu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139080","display_name":"Eiken Chemical (Japan)","ror":"https://ror.org/04ff4e804","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210139080"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Gakumatsu Ryu","raw_affiliation_strings":["Septeni Japan, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Septeni Japan, Inc.,Japan","institution_ids":["https://openalex.org/I4210139080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108274068","display_name":"Naoto Tanji","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139080","display_name":"Eiken Chemical (Japan)","ror":"https://ror.org/04ff4e804","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210139080"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naoto Tanji","raw_affiliation_strings":["Septeni Japan, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Septeni Japan, Inc.,Japan","institution_ids":["https://openalex.org/I4210139080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020705310","display_name":"Hiroyuki Seshime","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139080","display_name":"Eiken Chemical (Japan)","ror":"https://ror.org/04ff4e804","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210139080"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroyuki Seshime","raw_affiliation_strings":["Septeni Japan, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Septeni Japan, Inc.,Japan","institution_ids":["https://openalex.org/I4210139080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101476674","display_name":"Ling Xiao","orcid":"https://orcid.org/0000-0002-4650-8841"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ling Xiao","raw_affiliation_strings":["The University of Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048624196","display_name":"Toshihiko Yamasaki","orcid":"https://orcid.org/0000-0002-1784-2314"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshihiko Yamasaki","raw_affiliation_strings":["The University of Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5042344549"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35500663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1916","last_page":"1921"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.28859999775886536,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.28859999775886536,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.1859000027179718,"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/T11439","display_name":"Video Analysis and Summarization","score":0.08950000256299973,"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/metadata","display_name":"Metadata","score":0.6466000080108643},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5105000138282776},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5037999749183655},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.46950000524520874},{"id":"https://openalex.org/keywords/content","display_name":"Content (measure theory)","score":0.40689998865127563},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3847000002861023},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.3831999897956848},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.3668000102043152}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8202999830245972},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6466000080108643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5630999803543091},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5318999886512756},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5105000138282776},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.46950000524520874},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.43790000677108765},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3847000002861023},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.3668000102043152},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.29670000076293945},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc65261.2025.11249407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2020911841","https://openalex.org/W2074694452","https://openalex.org/W2090883204","https://openalex.org/W2723293840","https://openalex.org/W2794042075","https://openalex.org/W2949676527","https://openalex.org/W2970476646","https://openalex.org/W3159951727","https://openalex.org/W3185341429","https://openalex.org/W3199763349","https://openalex.org/W4205826145","https://openalex.org/W4296691800","https://openalex.org/W4385245566","https://openalex.org/W4389524251","https://openalex.org/W4396757491","https://openalex.org/W4400531810","https://openalex.org/W4400606525"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1,130],"presents":[2],"a":[3,125],"method":[4],"for":[5],"predicting":[6],"the":[7,40,75,101,114,134],"click-through":[8],"rate":[9],"(CTR)":[10],"of":[11,43,54,85],"video":[12,55],"advertisements":[13],"by":[14,119],"leveraging":[15],"high-level":[16],"semantic":[17,41,63,67,111],"content.":[18],"While":[19],"conventional":[20],"CTR":[21,71],"prediction":[22,102],"models":[23],"rely":[24],"primarily":[25],"on":[26],"metadata":[27],"such":[28],"as":[29,97],"ad":[30],"categories":[31],"or":[32],"user":[33],"behavior":[34],"logs,":[35],"our":[36],"approach":[37],"explicitly":[38],"incorporates":[39],"content":[42,136],"advertisements.":[44],"We":[45],"prompt":[46],"GPT-4o":[47],"to":[48,74,121,124],"generate":[49],"structured":[50],"natural":[51],"language":[52],"descriptions":[53],"scenes,":[56],"which":[57],"are":[58,94],"then":[59],"encoded":[60],"into":[61],"machine-interpretable":[62],"representations.":[64],"To":[65],"identify":[66],"features":[68,93,99,112],"that":[69,107,133],"reflect":[70],"drivers":[72],"specific":[73],"given":[76],"dataset,":[77],"we":[78],"employ":[79],"in-context":[80],"learning":[81],"with":[82],"curated":[83],"examples":[84],"high-":[86],"and":[87],"low-performing":[88],"ads.":[89],"The":[90],"resulting":[91],"interpretable":[92],"selectively":[95],"included":[96],"input":[98],"in":[100],"model.":[103,127],"Experimental":[104],"results":[105],"demonstrate":[106],"incorporating":[108],"these":[109],"dataset-specific":[110],"reduces":[113],"mean":[115],"squared":[116],"error":[117],"(MSE)":[118],"up":[120],"14.02%":[122],"compared":[123],"baseline":[126],"A":[128],"case":[129],"further":[131],"highlights":[132],"extracted":[135],"not":[137],"only":[138],"improves":[139],"predictive":[140],"performance":[141],"but":[142],"also":[143],"enhances":[144],"model":[145],"interpretability.":[146]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-11-28T00:00:00"}
