{"id":"https://openalex.org/W3009464535","doi":"https://doi.org/10.1145/3372338","title":"Improving Implicit Recommender Systems with Auxiliary Data","display_name":"Improving Implicit Recommender Systems with Auxiliary Data","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3009464535","doi":"https://doi.org/10.1145/3372338","mag":"3009464535"},"language":"en","primary_location":{"id":"doi:10.1145/3372338","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372338","pdf_url":null,"source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-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/A5052892856","display_name":"Jingtao Ding","orcid":"https://orcid.org/0000-0001-7985-6263"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingtao Ding","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110257299","display_name":"Guanghui Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guanghui Yu","raw_affiliation_strings":["Washington University in St. Louis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038668215","display_name":"Xiangnan He","orcid":"https://orcid.org/0000-0001-8472-7992"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangnan He","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044100655","display_name":"Depeng Jin","orcid":"https://orcid.org/0000-0003-0419-5514"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Depeng Jin","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052892856"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":5.8478,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.96333106,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"38","issue":"1","first_page":"1","last_page":"27"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9706000089645386,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9599999785423279,"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":0.8881691694259644},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8591166734695435},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.8579868078231812},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.8329542875289917},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.8142687678337097},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5678663849830627},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5501363277435303},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5422350764274597},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4085080921649933},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.407380610704422},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35430216789245605},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.34136074781417847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8881691694259644},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8591166734695435},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.8579868078231812},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8329542875289917},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.8142687678337097},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5678663849830627},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5501363277435303},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5422350764274597},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4085080921649933},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.407380610704422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35430216789245605},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34136074781417847},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372338","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372338","pdf_url":null,"source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G1757078732","display_name":null,"funder_award_id":"20031887521","funder_id":"https://openalex.org/F4320329777","funder_display_name":"Beijing National Research Center For Information Science And Technology"},{"id":"https://openalex.org/G8964616020","display_name":null,"funder_award_id":"61971267, 61972223, 61861136003, and 61621091","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329777","display_name":"Beijing National Research Center For Information Science And Technology","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W5384379","https://openalex.org/W1971838979","https://openalex.org/W1981844236","https://openalex.org/W1994389483","https://openalex.org/W1999973986","https://openalex.org/W2002834872","https://openalex.org/W2016666840","https://openalex.org/W2036254771","https://openalex.org/W2057763140","https://openalex.org/W2059512502","https://openalex.org/W2064754513","https://openalex.org/W2074290495","https://openalex.org/W2077927809","https://openalex.org/W2080371694","https://openalex.org/W2090679992","https://openalex.org/W2101409192","https://openalex.org/W2124187902","https://openalex.org/W2340502990","https://openalex.org/W2517217469","https://openalex.org/W2517388038","https://openalex.org/W2565948352","https://openalex.org/W2740450220","https://openalex.org/W2740701644","https://openalex.org/W2752904570","https://openalex.org/W2782111139","https://openalex.org/W2782298158","https://openalex.org/W2782696945","https://openalex.org/W2788730650","https://openalex.org/W2791723757","https://openalex.org/W2807891923","https://openalex.org/W2808446163","https://openalex.org/W2809112621","https://openalex.org/W2893359107","https://openalex.org/W2900229157","https://openalex.org/W2908900944","https://openalex.org/W2912057614","https://openalex.org/W2914041468","https://openalex.org/W2949676527","https://openalex.org/W2951369132","https://openalex.org/W2954123367","https://openalex.org/W2954622495","https://openalex.org/W2962989965","https://openalex.org/W2963323306","https://openalex.org/W2963884211","https://openalex.org/W2964239943","https://openalex.org/W2966199682","https://openalex.org/W2966799427","https://openalex.org/W2984589663"],"related_works":["https://openalex.org/W2112815677","https://openalex.org/W2767338541","https://openalex.org/W3216317163","https://openalex.org/W2011472225","https://openalex.org/W3000057026","https://openalex.org/W4313908216","https://openalex.org/W3048565508","https://openalex.org/W3163984363","https://openalex.org/W2963185427","https://openalex.org/W2342498029"],"abstract_inverted_index":{"Most":[0],"existing":[1],"recommender":[2,72],"systems":[3,73],"leverage":[4,105],"the":[5,10,32,39,60,113,129,146,160,177,195],"primary":[6,33,61],"feedback":[7,23,34,62],"only,":[8],"despite":[9],"fact":[11],"that":[12,191,206],"users":[13],"also":[14],"generate":[15],"a":[16,54,136,140,154,186],"large":[17],"amount":[18],"of":[19,63,102],"auxiliary":[20,81],"feedback.":[21],"These":[22],"usually":[24],"indicate":[25],"different":[26,90],"user":[27],"preferences":[28],"when":[29],"comparing":[30],"to":[31,37,104],"directly":[35],"used":[36],"optimize":[38],"system":[40],"performance.":[41],"For":[42],"example,":[43],"in":[44,100,144],"E-commerce":[45],"sites,":[46],"view":[47,82],"data":[48,83,197],"is":[49,192,219],"easily":[50],"accessible,":[51],"which":[52],"provides":[53],"valuable":[55],"yet":[56],"weaker":[57],"signal":[58],"than":[59,128],"purchase.":[64],"In":[65],"this":[66,150],"work,":[67],"we":[68,93,152],"improve":[69],"implicit":[70],"feedback-based":[71],"(dubbed":[74],"Implicit":[75],"Recommender":[76],"Systems":[77],")":[78],"by":[79,214],"integrating":[80],"into":[84],"matrix":[85,180],"factorization":[86],"(MF).":[87],"To":[88,148],"exploit":[89],"preference":[91],"levels,":[92],"propose":[94],"both":[95],"pointwise":[96,131],"and":[97,120,126],"pairwise":[98,114,137],"models":[99],"terms":[101],"how":[103],"users\u2019":[106],"viewing":[107],"behaviors.":[108],"The":[109],"latter":[110],"model":[111,174],"learns":[112],"ranking":[115],"relations":[116],"among":[117],"purchased,":[118],"viewed,":[119],"non-viewed":[121],"interactions,":[122],"being":[123],"more":[124],"effective":[125],"flexible":[127],"former":[130],"MF":[132,212],"method.":[133],"However,":[134],"such":[135],"formulation":[138],"poses":[139],"computational":[141],"efficiency":[142],"problem":[143],"learning":[145,156],"model.":[147],"address":[149],"problem,":[151],"design":[153],"new":[155],"algorithm":[157,170],"based":[158],"on":[159,194,201],"element-wise":[161],"Alternating":[162],"Least":[163],"Squares":[164],"(eALS)":[165],"learner.":[166],"Notably,":[167],"our":[168,207],"designed":[169],"can":[171],"efficiently":[172],"learn":[173],"parameters":[175],"from":[176],"whole":[178],"user-item":[179],"(including":[181],"all":[182],"missing":[183],"data),":[184],"with":[185],"rather":[187],"low":[188],"time":[189],"complexity":[190],"dependent":[193],"observed":[196],"only.":[198],"Extensive":[199],"experiments":[200],"two":[202],"real-world":[203],"datasets":[204],"demonstrate":[205],"method":[208],"outperforms":[209],"several":[210],"state-of-the-art":[211],"methods":[213],"6.43%\u223c":[215],"6.75%.":[216],"Our":[217],"implementation":[218],"available":[220],"at":[221],"https://github.com/dingjingtao/Auxiliary_enhanced_ALS.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-28T09:10:13.091523","created_date":"2025-10-10T00:00:00"}