{"id":"https://openalex.org/W2018049374","doi":"https://doi.org/10.1145/2020408.2020436","title":"Response prediction using collaborative filtering with hierarchies and side-information","display_name":"Response prediction using collaborative filtering with hierarchies and side-information","publication_year":2011,"publication_date":"2011-08-21","ids":{"openalex":"https://openalex.org/W2018049374","doi":"https://doi.org/10.1145/2020408.2020436","mag":"2018049374"},"language":"en","primary_location":{"id":"doi:10.1145/2020408.2020436","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020408.2020436","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 SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5049656925","display_name":"Aditya Krishna Menon","orcid":"https://orcid.org/0000-0002-1352-7106"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aditya Krishna Menon","raw_affiliation_strings":["University of California, San Diego, La Jolla, CA, USA","University of California - San Diego, La Jolla, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California - San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001809321","display_name":"Krishna-Prasad Chitrapura","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142485","display_name":"QNu Labs (India)","ror":"https://ror.org/03xcp1s96","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210142485"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krishna-Prasad Chitrapura","raw_affiliation_strings":["Yahoo! Labs, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo! Labs, Bengaluru, India","institution_ids":["https://openalex.org/I4210142485"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100896317","display_name":"Sachin Garg","orcid":"https://orcid.org/0009-0004-6719-5866"},"institutions":[{"id":"https://openalex.org/I4210142485","display_name":"QNu Labs (India)","ror":"https://ror.org/03xcp1s96","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210142485"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sachin Garg","raw_affiliation_strings":["Yahoo! Labs, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo! Labs, Bengaluru, India","institution_ids":["https://openalex.org/I4210142485"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049420901","display_name":"Deepak Agarwal","orcid":"https://orcid.org/0000-0003-2881-1254"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepak Agarwal","raw_affiliation_strings":["Yahoo! Research, Santa Clara, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo! Research, Santa Clara, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022658391","display_name":"Nagaraj Kota","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142485","display_name":"QNu Labs (India)","ror":"https://ror.org/03xcp1s96","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210142485"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nagaraj Kota","raw_affiliation_strings":["Yahoo! Labs, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo! Labs, Bengaluru, India","institution_ids":["https://openalex.org/I4210142485"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":138,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"149"},"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/T10609","display_name":"Digital Marketing and Social Media","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7732031345367432},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7537109851837158},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6799572110176086},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6182212233543396},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.5843186378479004},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.49477142095565796},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4739375412464142},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46117910742759705},{"id":"https://openalex.org/keywords/web-page","display_name":"Web page","score":0.4590339660644531},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4416219890117645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4351193904876709},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.43011274933815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4280463755130768},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4166339039802551},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3898904025554657},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.379020094871521},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21205273270606995},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17010372877120972},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.09182918071746826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732031345367432},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7537109851837158},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6799572110176086},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6182212233543396},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.5843186378479004},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.49477142095565796},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4739375412464142},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46117910742759705},{"id":"https://openalex.org/C21959979","wikidata":"https://www.wikidata.org/wiki/Q36774","display_name":"Web page","level":2,"score":0.4590339660644531},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4416219890117645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4351193904876709},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.43011274933815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4280463755130768},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4166339039802551},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3898904025554657},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.379020094871521},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21205273270606995},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17010372877120972},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.09182918071746826},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2020408.2020436","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020408.2020436","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 SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1484579967","https://openalex.org/W1750800295","https://openalex.org/W1969675113","https://openalex.org/W1988139567","https://openalex.org/W2017894137","https://openalex.org/W2054141820","https://openalex.org/W2054553473","https://openalex.org/W2070996757","https://openalex.org/W2071488943","https://openalex.org/W2090883204","https://openalex.org/W2101409192","https://openalex.org/W2105831665","https://openalex.org/W2106502572","https://openalex.org/W2113802117","https://openalex.org/W2120166550","https://openalex.org/W2123082179","https://openalex.org/W2129606454","https://openalex.org/W2137245235","https://openalex.org/W2149490995","https://openalex.org/W2162979096","https://openalex.org/W2997904722"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W2037504162","https://openalex.org/W4390394189","https://openalex.org/W2792706544","https://openalex.org/W3109911900","https://openalex.org/W1568451138","https://openalex.org/W2539013788","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031"],"abstract_inverted_index":{"In":[0,24],"online":[1],"advertising,":[2],"response":[3,30,66,141,173],"prediction":[4,31,142],"is":[5,15],"the":[6,10,57,69,78,129,137,149],"problem":[7,37],"of":[8,38,71,80,94,131,140],"estimating":[9],"probability":[11],"that":[12,165],"an":[13],"advertisement":[14],"clicked":[16],"when":[17],"displayed":[18],"on":[19,158],"a":[20,36,90,100],"content":[21],"publisher's":[22],"webpage.":[23],"this":[25,110],"paper,":[26],"we":[27,135],"show":[28,108,164],"how":[29,109],"can":[32,112],"be":[33,113],"viewed":[34],"as":[35],"matrix":[39,47,83,91],"completion,":[40],"and":[41,65,77,97,123,151],"propose":[42],"to":[43,104],"solve":[44],"it":[45],"using":[46,89],"factorization":[48,92,111,155],"techniques":[49],"from":[50],"collaborative":[51],"filtering":[52],"(CF).":[53],"We":[54,85,107],"point":[55],"out":[56],"two":[58],"crucial":[59],"differences":[60],"between":[61],"standard":[62],"CF":[63],"problems":[64],"prediction,":[67],"namely":[68],"requirement":[70],"predicting":[72],"probabilities":[73],"rather":[74],"than":[75],"scores,":[76],"issue":[79],"confidence":[81],"in":[82],"entries.":[84],"address":[86],"these":[87],"issues":[88],"analogue":[93],"logistic":[95],"regression,":[96],"by":[98,144],"applying":[99],"principled":[101],"confidence-weighting":[102],"scheme":[103],"its":[105],"objective.":[106],"seamlessly":[114],"combined":[115],"with":[116],"explicit":[117],"features":[118],"or":[119],"side-information":[120],"for":[121,172],"pages":[122,150],"ads,":[124],"which":[125],"let":[126],"us":[127],"combine":[128],"benefits":[130],"both":[132],"approaches.":[133],"Finally,":[134],"combat":[136],"extreme":[138],"sparsity":[139],"data":[143],"incorporating":[145],"hierarchical":[146],"information":[147],"about":[148],"ads":[152],"into":[153],"our":[154,166],"model.":[156],"Experiments":[157],"three":[159],"very":[160],"large":[161],"real-world":[162],"datasets":[163],"model":[167],"outperforms":[168],"current":[169],"state-of-the-art":[170],"methods":[171],"prediction.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":17},{"year":2016,"cited_by_count":13},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":16},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}