{"id":"https://openalex.org/W2998207486","doi":"https://doi.org/10.1145/3336191.3371785","title":"Interpretable Click-Through Rate Prediction through Hierarchical Attention","display_name":"Interpretable Click-Through Rate Prediction through Hierarchical Attention","publication_year":2020,"publication_date":"2020-01-20","ids":{"openalex":"https://openalex.org/W2998207486","doi":"https://doi.org/10.1145/3336191.3371785","mag":"2998207486"},"language":"en","primary_location":{"id":"doi:10.1145/3336191.3371785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371785","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100455067","display_name":"Zeyu Li","orcid":"https://orcid.org/0000-0002-3560-7844"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zeyu Li","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019907526","display_name":"Wei Cheng","orcid":"https://orcid.org/0000-0002-0874-9927"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["NEC Laboratories America, Inc., Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Inc., Princeton, NJ, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103867130","display_name":"Yang Chen","orcid":"https://orcid.org/0009-0001-7995-4119"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Chen","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456786","display_name":"Haifeng Chen","orcid":"https://orcid.org/0000-0002-9363-738X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haifeng Chen","raw_affiliation_strings":["NEC Laboratories America, Inc., Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Inc., Princeton, NJ, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100455067"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":21.1425,"has_fulltext":true,"cited_by_count":121,"citation_normalized_percentile":{"value":0.99367261,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"313","last_page":"321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9958000183105469,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8029477000236511},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.6553277969360352},{"id":"https://openalex.org/keywords/polysemy","display_name":"Polysemy","score":0.5942199230194092},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.592416524887085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5632278323173523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5483643412590027},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46259987354278564},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.18215146660804749}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8029477000236511},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.6553277969360352},{"id":"https://openalex.org/C2780276568","wikidata":"https://www.wikidata.org/wiki/Q191928","display_name":"Polysemy","level":2,"score":0.5942199230194092},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.592416524887085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5632278323173523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5483643412590027},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46259987354278564},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.18215146660804749},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3336191.3371785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3336191.3371785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G3413281528","display_name":null,"funder_award_id":"NST DGE-1829071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7189626769","display_name":null,"funder_award_id":"1829071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8442901025","display_name":null,"funder_award_id":"DGE-1829071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2998207486.pdf","grobid_xml":"https://content.openalex.org/works/W2998207486.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1561225015","https://openalex.org/W1771178157","https://openalex.org/W1928906481","https://openalex.org/W2076618162","https://openalex.org/W2090883204","https://openalex.org/W2133564696","https://openalex.org/W2194775991","https://openalex.org/W2219888463","https://openalex.org/W2255575265","https://openalex.org/W2295739661","https://openalex.org/W2463565445","https://openalex.org/W2470673105","https://openalex.org/W2475334473","https://openalex.org/W2505972586","https://openalex.org/W2509235963","https://openalex.org/W2517540742","https://openalex.org/W2548570154","https://openalex.org/W2575337496","https://openalex.org/W2606642831","https://openalex.org/W2626778328","https://openalex.org/W2723293840","https://openalex.org/W2740098507","https://openalex.org/W2750075801","https://openalex.org/W2787933113","https://openalex.org/W2793768763","https://openalex.org/W2808310571","https://openalex.org/W2889616363","https://openalex.org/W2903533908","https://openalex.org/W2951001079","https://openalex.org/W2963271116","https://openalex.org/W2963609017","https://openalex.org/W2963847595","https://openalex.org/W2963869731","https://openalex.org/W2964052347","https://openalex.org/W2964069537","https://openalex.org/W3101704389","https://openalex.org/W3104030692","https://openalex.org/W4234552385"],"related_works":["https://openalex.org/W2376040010","https://openalex.org/W2613880225","https://openalex.org/W2788559978","https://openalex.org/W2358036664","https://openalex.org/W3164948662","https://openalex.org/W3105157121","https://openalex.org/W3104067163","https://openalex.org/W3153597579","https://openalex.org/W4382049132","https://openalex.org/W3159661535"],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,123],"is":[4,85],"a":[5,96],"critical":[6],"task":[7],"in":[8,70,81],"online":[9],"advertising":[10],"and":[11,42,49,52,147,154],"marketing.":[12],"For":[13],"this":[14,89],"problem,":[15],"existing":[16,65],"approaches,":[17],"with":[18,98,136],"shallow":[19],"or":[20],"deep":[21],"architectures,":[22],"have":[23,67],"three":[24],"major":[25],"drawbacks.":[26],"First,":[27],"they":[28],"typically":[29],"lack":[30],"persuasive":[31],"rationales":[32],"to":[33,47],"explain":[34],"the":[35,38,76,122,152],"outcomes":[36],"of":[37,78,106,121,156],"models.":[39],"Unexplainable":[40],"predictions":[41],"recommendations":[43],"may":[44,59],"be":[45],"difficult":[46],"validate":[48],"thus":[50],"unreliable":[51],"untrustworthy.":[53],"In":[54,88],"many":[55],"applications,":[56],"inappropriate":[57],"suggestions":[58],"even":[60],"bring":[61],"severe":[62],"consequences.":[63],"Second,":[64],"approaches":[66],"poor":[68],"efficiency":[69,155],"analyzing":[71],"high-order":[72,127],"feature":[73,79,102,128],"interactions.":[74],"Third,":[75],"polysemy":[77],"interactions":[80,129],"different":[82],"semantic":[83],"subspaces":[84],"largely":[86],"ignored.":[87],"paper,":[90],"we":[91],"propose":[92],"InterHAt":[93,125],"that":[94],"employs":[95],"Transformer":[97],"multi-head":[99],"self-attention":[100],"for":[101,113],"learning.":[103],"On":[104],"top":[105],"that,":[107],"hierarchical":[108],"attention":[109],"layers":[110],"are":[111],"utilized":[112],"predicting":[114],"CTR":[115],"while":[116],"simultaneously":[117],"providing":[118],"interpretable":[119],"insights":[120],"results.":[124],"captures":[126],"by":[130],"an":[131],"efficient":[132],"attentional":[133],"aggregation":[134],"strategy":[135],"low":[137],"computational":[138],"complexity.":[139],"Extensive":[140],"experiments":[141],"on":[142],"four":[143],"public":[144],"real":[145],"datasets":[146],"one":[148],"synthetic":[149],"dataset":[150],"demonstrate":[151],"effectiveness":[153],"InterHAt.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
