{"id":"https://openalex.org/W3210317057","doi":"https://doi.org/10.1145/3459637.3482331","title":"Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation","display_name":"Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3210317057","doi":"https://doi.org/10.1145/3459637.3482331","mag":"3210317057"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482331","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482331","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","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/A5062499743","display_name":"Ke Tu","orcid":"https://orcid.org/0009-0009-4922-1684"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ke Tu","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009228005","display_name":"Peng Cui","orcid":"https://orcid.org/0000-0003-2957-8511"},"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":"Peng Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008647105","display_name":"Daixin Wang","orcid":"https://orcid.org/0000-0002-5166-0362"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daixin Wang","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346697","display_name":"Zhiqiang Zhang","orcid":"https://orcid.org/0000-0001-7857-175X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiqiang Zhang","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781212","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-5822-8233"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019180907","display_name":"Qi Yuan","orcid":"https://orcid.org/0000-0002-1342-3374"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan Qi","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339293","display_name":"Wenwu Zhu","orcid":"https://orcid.org/0000-0003-2236-9290"},"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":"Wenwu Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5062499743"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.915,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.98106394,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1834","last_page":"1843"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9909999966621399,"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":0.8176732063293457},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5782981514930725},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5573463439941406},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5464774370193481},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.47234976291656494},{"id":"https://openalex.org/keywords/knowledge-engineering","display_name":"Knowledge engineering","score":0.4478342831134796},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.43063411116600037},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.418667197227478},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41293513774871826},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36461693048477173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33011364936828613},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10821127891540527},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08833575248718262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8176732063293457},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5782981514930725},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5573463439941406},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5464774370193481},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.47234976291656494},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.4478342831134796},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.43063411116600037},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.418667197227478},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41293513774871826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36461693048477173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33011364936828613},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10821127891540527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08833575248718262},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482331","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482331","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W205829674","https://openalex.org/W1533230146","https://openalex.org/W1564094940","https://openalex.org/W1570159982","https://openalex.org/W2010187764","https://openalex.org/W2028988057","https://openalex.org/W2029694543","https://openalex.org/W2040367556","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2101491865","https://openalex.org/W2127795553","https://openalex.org/W2140310134","https://openalex.org/W2158515176","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2468907370","https://openalex.org/W2509893387","https://openalex.org/W2515144511","https://openalex.org/W2519887557","https://openalex.org/W2583803680","https://openalex.org/W2604314403","https://openalex.org/W2624431344","https://openalex.org/W2728059831","https://openalex.org/W2743104969","https://openalex.org/W2759136286","https://openalex.org/W2767774008","https://openalex.org/W2783279085","https://openalex.org/W2792839191","https://openalex.org/W2884134047","https://openalex.org/W2911286998","https://openalex.org/W2911778742","https://openalex.org/W2913560138","https://openalex.org/W2945623882","https://openalex.org/W2950275995","https://openalex.org/W2962992837","https://openalex.org/W2963323306","https://openalex.org/W2963403868","https://openalex.org/W2963432357","https://openalex.org/W2963858333","https://openalex.org/W2963911286","https://openalex.org/W2964116313","https://openalex.org/W2966349618","https://openalex.org/W3011667710","https://openalex.org/W3021393704","https://openalex.org/W3035011799","https://openalex.org/W3043738669","https://openalex.org/W3098087397","https://openalex.org/W3099726771","https://openalex.org/W3106439716","https://openalex.org/W3199689927"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W3154803703","https://openalex.org/W4205426038","https://openalex.org/W4206407708","https://openalex.org/W1753267753"],"abstract_inverted_index":{"Knowledge":[0],"graph":[1,37,52,86,132,156],"is":[2,79],"generally":[3],"incorporated":[4],"into":[5,87,133],"recommender":[6,44,89],"systems":[7],"to":[8,13,38,48,53,62,83,99,157,170],"improve":[9],"overall":[10,172],"performance.":[11,173],"Due":[12],"the":[14,19,35,50,55,60,64,69,101,107,112,116,130,134,139,150,154,180,186],"generalization":[15],"and":[16,58,115,168],"scale":[17],"of":[18,182],"knowledge":[20,23,36,41,51,61,85,117,131,155],"graph,":[21],"most":[22],"relationships":[24,42],"are":[25],"not":[26],"helpful":[27],"for":[28],"a":[29,88,94,121,124,145],"target":[30],"user-item":[31,113,125],"prediction.":[32],"To":[33,67],"exploit":[34],"capture":[39,63],"target-specific":[40,135,159],"in":[43],"systems,":[45],"we":[46,71,92,127,152,163],"need":[47],"distill":[49,129],"reserve":[54],"useful":[56],"information":[57],"refine":[59,153],"users'":[65],"preferences.":[66],"address":[68],"issues,":[70],"propose":[72],"Knowledge-aware":[73],"Conditional":[74],"Attention":[75],"Networks":[76],"(KCAN),":[77],"which":[78,105],"an":[80],"end-to-end":[81],"model":[82],"incorporate":[84],"system.":[90],"Specifically,":[91],"use":[93],"knowledge-aware":[95,140],"attention":[96,147],"propagation":[97],"manner":[98],"obtain":[100,158],"node":[102,160],"representation":[103],"first,":[104],"captures":[106],"global":[108],"semantic":[109],"similarity":[110],"on":[111,138,149,176],"network":[114],"graph.":[118],"Then":[119],"given":[120],"target,":[122],"i.e.,":[123],"pair,":[126],"automatically":[128],"subgraph":[136],"based":[137],"attention.":[141],"Afterward,":[142],"by":[143],"applying":[144],"conditional":[146],"aggregation":[148],"subgraph,":[151],"representations.":[161],"Therefore,":[162],"can":[164],"gain":[165],"both":[166],"representability":[167],"personalization":[169],"achieve":[171],"Experimental":[174],"results":[175],"real-world":[177],"datasets":[178],"demonstrate":[179],"effectiveness":[181],"our":[183],"framework":[184],"over":[185],"state-of-the-art":[187],"algorithms.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
