{"id":"https://openalex.org/W2903340942","doi":"https://doi.org/10.1609/aaai.v33i01.33013622","title":"Explainable Recommendation through Attentive Multi-View Learning","display_name":"Explainable Recommendation through Attentive Multi-View Learning","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2903340942","doi":"https://doi.org/10.1609/aaai.v33i01.33013622","mag":"2903340942"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33013622","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33013622","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4243/4121","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4243/4121","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042921648","display_name":"Jingyue Gao","orcid":"https://orcid.org/0009-0003-3154-5206"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyue Gao","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101524540","display_name":"Xiting Wang","orcid":"https://orcid.org/0000-0001-5768-1095"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiting Wang","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055336632","display_name":"Yasha Wang","orcid":"https://orcid.org/0000-0002-8026-9688"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yasha Wang","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044651577","display_name":"Xing Xie","orcid":"https://orcid.org/0000-0002-8608-8482"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xie","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042921648"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":31.7,"has_fulltext":true,"cited_by_count":130,"citation_normalized_percentile":{"value":0.99637681,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"33","issue":"01","first_page":"3622","last_page":"3629"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.996399998664856,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9908999800682068,"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":0.8451054096221924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6709136962890625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6441144943237305},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.6202943921089172},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5851245522499084},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5267944931983948},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5044378042221069},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5017333030700684},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47806456685066223},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47254109382629395},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4686386287212372},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4493817985057831},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4146379828453064},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20226430892944336}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8451054096221924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6709136962890625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6441144943237305},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.6202943921089172},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5851245522499084},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5267944931983948},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5044378042221069},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5017333030700684},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47806456685066223},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47254109382629395},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4686386287212372},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4493817985057831},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4146379828453064},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20226430892944336},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","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/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v33i01.33013622","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33013622","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4243/4121","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33013622","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33013622","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4243/4121","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5426643869","display_name":null,"funder_award_id":"61772045","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","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"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2903340942.pdf","grobid_xml":"https://content.openalex.org/works/W2903340942.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1602639018","https://openalex.org/W1670132599","https://openalex.org/W1970972959","https://openalex.org/W1971389588","https://openalex.org/W1983328081","https://openalex.org/W1994389483","https://openalex.org/W2028988057","https://openalex.org/W2061873838","https://openalex.org/W2077335893","https://openalex.org/W2116959421","https://openalex.org/W2123427850","https://openalex.org/W2135029798","https://openalex.org/W2137245235","https://openalex.org/W2138605095","https://openalex.org/W2142972908","https://openalex.org/W2152184085","https://openalex.org/W2157881433","https://openalex.org/W2509893387","https://openalex.org/W2557074642","https://openalex.org/W2575006718","https://openalex.org/W2583875861","https://openalex.org/W2605350416","https://openalex.org/W2741249238","https://openalex.org/W2743904806","https://openalex.org/W2788376297","https://openalex.org/W2788730650","https://openalex.org/W2808925008","https://openalex.org/W2897405591","https://openalex.org/W2951441387","https://openalex.org/W6636266071","https://openalex.org/W6670102205","https://openalex.org/W6680012447","https://openalex.org/W6680451568","https://openalex.org/W6683417489","https://openalex.org/W6735804486"],"related_works":["https://openalex.org/W4386781444","https://openalex.org/W3092950680","https://openalex.org/W2150182025","https://openalex.org/W4246980185","https://openalex.org/W4317039510","https://openalex.org/W3197542405","https://openalex.org/W2418190244","https://openalex.org/W4238861846","https://openalex.org/W2976657239","https://openalex.org/W1985727224"],"abstract_inverted_index":{"Recommender":[0],"systems":[1],"have":[2,34],"been":[3],"playing":[4],"an":[5,59,81,86,118],"increasingly":[6],"important":[7],"role":[8],"in":[9,41,103,180],"our":[10,175],"daily":[11],"life":[12],"due":[13],"to":[14,49,79,127,168],"the":[15,38,51,65,96,104,148],"explosive":[16],"growth":[17],"of":[18,37,67,182],"information.":[19],"Accuracy":[20],"and":[21,33,55,71,94,109,130,139,162,185],"explainability":[22,56],"are":[23],"two":[24],"core":[25],"aspects":[26],"when":[27],"we":[28,47,116,150],"evaluate":[29],"a":[30,156,164],"recommendation":[31],"model":[32,62,97,176],"become":[35],"one":[36],"fundamental":[39],"trade-offs":[40],"machine":[42],"learning.":[43],"In":[44],"this":[45],"paper,":[46],"propose":[48,117,163],"alleviate":[50],"trade-off":[52],"between":[53],"accuracy":[54,98,184],"by":[57,99,133],"developing":[58],"explainable":[60,73,87],"deep":[61,68,88],"that":[63,174],"combines":[64],"advantages":[66],"learning-based":[69],"models":[70],"existing":[72],"methods.":[74],"The":[75,123],"basic":[76],"idea":[77],"is":[78],"build":[80],"initial":[82],"network":[83],"based":[84],"on":[85],"hierarchy":[89,105],"(e.g.,":[90,106],"Microsoft":[91],"Concept":[92],"Graph)":[93],"improve":[95],"optimizing":[100],"key":[101],"variables":[102],"node":[107,159],"importance":[108],"relevance).":[110],"To":[111,143],"ensure":[112],"accurate":[113],"rating":[114],"prediction,":[115],"attentive":[119],"multi-view":[120],"learning":[121],"framework.":[122],"framework":[124],"enables":[125],"us":[126],"handle":[128],"sparse":[129],"noisy":[131],"data":[132],"co-regularizing":[134],"among":[135],"different":[136],"feature":[137],"levels":[138],"combining":[140],"predictions":[141],"attentively.":[142],"mine":[144],"readable":[145],"explanations":[146],"from":[147],"hierarchy,":[149],"formulate":[151],"personalized":[152],"explanation":[153],"generation":[154],"as":[155],"constrained":[157],"tree":[158],"selection":[160],"problem":[161],"dynamic":[165],"programming":[166],"algorithm":[167],"solve":[169],"it.":[170],"Experimental":[171],"results":[172],"show":[173],"outperforms":[177],"state-of-the-art":[178],"methods":[179],"terms":[181],"both":[183],"explainability.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":7}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
