{"id":"https://openalex.org/W2099422090","doi":"https://doi.org/10.1145/2433396.2433445","title":"Latent factor models with additive and hierarchically-smoothed user preferences","display_name":"Latent factor models with additive and hierarchically-smoothed user preferences","publication_year":2013,"publication_date":"2013-02-04","ids":{"openalex":"https://openalex.org/W2099422090","doi":"https://doi.org/10.1145/2433396.2433445","mag":"2099422090"},"language":"en","primary_location":{"id":"doi:10.1145/2433396.2433445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth 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/A5009154893","display_name":"Amr Ahmed","orcid":"https://orcid.org/0000-0002-7749-7911"},"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":true,"raw_author_name":"Amr Ahmed","raw_affiliation_strings":["Google Inc., Mountain View, USA","Google Inc. , Mountain View, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc. , Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057677675","display_name":"Bhargav Kanagal","orcid":null},"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":"Bhargav Kanagal","raw_affiliation_strings":["Google Inc., Mountain View, USA","Google Inc. , Mountain View, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc. , Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042339206","display_name":"Sandeep Pandey","orcid":"https://orcid.org/0000-0003-1995-6515"},"institutions":[{"id":"https://openalex.org/I113979032","display_name":"Twitter (United States)","ror":"https://ror.org/04wt43v05","country_code":"US","type":"company","lineage":["https://openalex.org/I113979032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sandeep Pandey","raw_affiliation_strings":["Twitter, San Fransisco, USA","Twitter, San Fransisco, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Twitter, San Fransisco, USA","institution_ids":["https://openalex.org/I113979032"]},{"raw_affiliation_string":"Twitter, San Fransisco, USA#TAB#","institution_ids":["https://openalex.org/I113979032"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037908462","display_name":"Vanja Josifovski","orcid":null},"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":"Vanja Josifovski","raw_affiliation_strings":["Google Inc., Mountain View, USA","Google Inc. , Mountain View, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc. , Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079590464","display_name":"Llu\u00eds Garcia Pueyo","orcid":null},"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":"Lluis Garcia Pueyo","raw_affiliation_strings":["Google inc., Mountain View, USA","Google Inc. , Mountain View, USA"],"affiliations":[{"raw_affiliation_string":"Google inc., Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc. , Mountain View, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090263495","display_name":"Jeff Yuan","orcid":null},"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":"Jeff Yuan","raw_affiliation_strings":["Yahoo! Research, Sunnyvale, USA"],"affiliations":[{"raw_affiliation_string":"Yahoo! Research, Sunnyvale, USA","institution_ids":["https://openalex.org/I4210134091"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5009154893"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":15.8756,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.9873988,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"385","last_page":"394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9822999835014343,"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/T10028","display_name":"Topic Modeling","score":0.9661999940872192,"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/recommender-system","display_name":"Recommender system","score":0.849982500076294},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.8351908922195435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8007890582084656},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6409668922424316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.562375545501709},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.537112295627594},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5285935401916504},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5159376263618469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5142411589622498},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5094452500343323},{"id":"https://openalex.org/keywords/factor-analysis","display_name":"Factor analysis","score":0.464947372674942},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.429429829120636},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4166911840438843},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41381022334098816}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.849982500076294},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.8351908922195435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8007890582084656},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6409668922424316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.562375545501709},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.537112295627594},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5285935401916504},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5159376263618469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5142411589622498},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5094452500343323},{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.464947372674942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.429429829120636},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4166911840438843},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41381022334098816},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2433396.2433445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth ACM international conference on Web search and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.296.4565","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.4565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~amahmed/papers/LFM_with_user_preferences_WSDM_2013.pdf","raw_type":"text"}],"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/W82218792","https://openalex.org/W281665770","https://openalex.org/W1994389483","https://openalex.org/W2001530795","https://openalex.org/W2041022395","https://openalex.org/W2054141820","https://openalex.org/W2054553473","https://openalex.org/W2080320419","https://openalex.org/W2089349245","https://openalex.org/W2119490247","https://openalex.org/W2121154640","https://openalex.org/W2123082179","https://openalex.org/W2138996412","https://openalex.org/W2140310134","https://openalex.org/W2142534468","https://openalex.org/W2153102839","https://openalex.org/W2155736969","https://openalex.org/W2158266063","https://openalex.org/W2165949563","https://openalex.org/W2341535507","https://openalex.org/W6603355732","https://openalex.org/W6703949738"],"related_works":["https://openalex.org/W1484355083","https://openalex.org/W2772628444","https://openalex.org/W4220714703","https://openalex.org/W2735929803","https://openalex.org/W2170391450","https://openalex.org/W2098758514","https://openalex.org/W3008845055","https://openalex.org/W2041004656","https://openalex.org/W4376854386","https://openalex.org/W1966742602"],"abstract_inverted_index":{"Items":[0],"in":[1,45,84],"recommender":[2,48,85],"systems":[3],"are":[4,24],"usually":[5],"associated":[6],"with":[7,139,182],"annotated":[8],"attributes:":[9],"for":[10,15,18,31],"e.g.,":[11],"brand":[12],"and":[13,27,50,65,156,171,186,216],"price":[14],"products;":[16],"agency":[17],"news":[19],"articles,":[20],"etc.":[21],"Such":[22],"attributes":[23,42,167,173],"highly":[25],"informative":[26],"must":[28],"be":[29],"exploited":[30],"accurate":[32],"recommendation.":[33],"While":[34],"learning":[35],"a":[36,100,122,129,133],"user":[37,116,180],"preference":[38,136],"model":[39,69,102,126,137,210],"over":[40,149],"these":[41,147],"can":[43,51,163],"result":[44],"an":[46,153,220],"interpretable":[47],"system":[49,192],"hands":[52],"the":[53,66,73,106,111,119,150,179,183,188,195,224],"cold":[54],"start":[55],"problem,":[56],"it":[57],"suffers":[58],"from":[59],"two":[60],"major":[61],"drawbacks:":[62],"data":[63],"sparsity":[64],"inability":[67],"to":[68],"random":[70],"effects.":[71],"On":[72],"other":[74],"hand,":[75],"latent-factor":[76,184],"collaborative":[77,190],"filtering":[78,191],"models":[79,185],"have":[80],"shown":[81],"great":[82],"promise":[83],"systems;":[86],"however,":[87],"its":[88],"performance":[89],"on":[90],"rare":[91],"items":[92],"is":[93],"poor.":[94],"In":[95,200],"this":[96],"paper":[97],"we":[98,145,205,217],"propose":[99],"novel":[101],"LFUM,":[103],"which":[104],"provides":[105],"advantages":[107],"of":[108,110,132,226,229],"both":[109,165],"above":[112],"models.":[113],"We":[114,177],"learn":[115],"preferences":[117,148,181],"(over":[118],"attributes)":[120],"using":[121,152,194],"personalized":[123],"Bayesian":[124],"hierarchical":[125],"that":[127,207],"uses":[128],"combination(additive":[130],"model)":[131],"globally":[134],"learned":[135],"along":[138],"user-specific":[140],"preferences.":[141],"To":[142],"combat":[143],"data-sparsity,":[144],"smooth":[146],"item-taxonomy":[151],"efficient":[154],"forward-filtering":[155],"backward-smoothing":[157],"inference":[158,161],"algorithm.":[159,199],"Our":[160],"algorithms":[162],"handle":[164],"discrete":[166],"(e.g.,":[168,174],"item":[169,175],"brands)":[170],"continuous":[172],"prices).":[176],"combine":[178],"train":[187],"resulting":[189],"end-to-end":[193],"successful":[196],"BPR":[197],"ranking":[198],"our":[201,208,230],"extensive":[202],"experimental":[203],"analysis,":[204],"show":[206],"proposed":[209],"outperforms":[211],"several":[212],"commonly":[213],"used":[214],"baselines":[215],"carry":[218],"out":[219],"ablation":[221],"study":[222],"showing":[223],"benefits":[225],"each":[227],"component":[228],"model.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":7}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
