{"id":"https://openalex.org/W2507789193","doi":"https://doi.org/10.1145/2959100.2959139","title":"A Scalable Approach for Periodical Personalized Recommendations","display_name":"A Scalable Approach for Periodical Personalized Recommendations","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2507789193","doi":"https://doi.org/10.1145/2959100.2959139","mag":"2507789193"},"language":"en","primary_location":{"id":"doi:10.1145/2959100.2959139","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2959100.2959139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Conference on Recommender Systems","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/A5100763095","display_name":"Zhen Qin","orcid":"https://orcid.org/0000-0001-7857-9719"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhen Qin","raw_affiliation_strings":["Ticketmaster, Hollywood, CA, USA"],"affiliations":[{"raw_affiliation_string":"Ticketmaster, Hollywood, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024148586","display_name":"Ish Rishabh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ish Rishabh","raw_affiliation_strings":["Ticketmaster, Hollywood, CA, USA"],"affiliations":[{"raw_affiliation_string":"Ticketmaster, Hollywood, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034902922","display_name":"John Carnahan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"John Carnahan","raw_affiliation_strings":["Ticketmaster, Hollywood, CA, USA"],"affiliations":[{"raw_affiliation_string":"Ticketmaster, Hollywood, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100763095"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7181,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77297619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9926999807357788,"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":0.7249404191970825},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6226459741592407},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.16110292077064514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7249404191970825},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6226459741592407},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.16110292077064514}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2959100.2959139","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2959100.2959139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1539833865","https://openalex.org/W1613456526","https://openalex.org/W1973987653","https://openalex.org/W1985759455","https://openalex.org/W2029604041","https://openalex.org/W2070996757","https://openalex.org/W2101409192","https://openalex.org/W2108738385","https://openalex.org/W2112420033","https://openalex.org/W2150102617","https://openalex.org/W2161676175","https://openalex.org/W2963433763"],"related_works":["https://openalex.org/W1527726406","https://openalex.org/W2364921833","https://openalex.org/W2388030554","https://openalex.org/W2302028273","https://openalex.org/W1525643724","https://openalex.org/W2382623646","https://openalex.org/W3087771547","https://openalex.org/W2595032667","https://openalex.org/W2333420780","https://openalex.org/W2795601052"],"abstract_inverted_index":{"We":[0,50,111],"develop":[1],"a":[2,19,97,123],"highly":[3],"scalable":[4],"and":[5,47,71,90,108,119],"effective":[6],"contextual":[7],"bandit":[8],"approach":[9,126],"towards":[10],"periodical":[11],"personalized":[12],"recommendations.":[13],"The":[14,82],"online":[15,109],"bootstrapping-based":[16],"technique":[17],"provides":[18],"principled":[20],"way":[21],"for":[22],"UCB-type":[23],"exploitation-exploration":[24],"algorithms,":[25],"while":[26],"being":[27],"able":[28],"to":[29,36,54],"handle":[30,55],"arbitrary":[31,56],"sized":[32,57],"datasets,":[33],"well":[34],"suited":[35],"learn":[37],"the":[38,138],"ever":[39],"evolving":[40],"user":[41],"preference":[42],"drift":[43],"from":[44],"streaming":[45],"data,":[46],"essentially":[48],"parameter-free.":[49],"further":[51],"introduce":[52],"techniques":[53],"feature":[58,61],"spaces":[59],"using":[60],"hashing,":[62],"leverage":[63],"existing":[64],"state-of-art":[65],"machine":[66],"learning":[67,69],"via":[68],"reduction,":[70],"increase":[72],"cache":[73],"hits":[74],"by":[75],"managing":[76],"bootstrapped":[77],"models":[78],"in":[79,105,127],"memory":[80],"effectively.":[81],"resulted":[83],"model":[84],"trains":[85],"on":[86,96,137],"millions":[87],"of":[88,92],"examples":[89],"billions":[91],"features":[93],"within":[94],"minutes":[95],"single":[98],"personal":[99],"computer.":[100],"It":[101],"shows":[102],"persistent":[103],"performance":[104],"both":[106],"offline":[107],"evaluation.":[110],"observe":[112],"around":[113],"10\\%":[114],"click":[115],"through":[116],"rate":[117],"(CTR)":[118],"conversion":[120],"lift":[121],"over":[122],"collaborative":[124],"filtering":[125],"real-world":[128],"A/B":[129],"testing":[130],"across":[131],"more":[132],"than":[133],"40":[134],"million":[135],"users":[136],"major":[139],"Ticketmaster":[140],"email":[141],"recommendation":[142],"product.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}