{"id":"https://openalex.org/W3035143414","doi":"https://doi.org/10.1145/3397271.3401123","title":"Neighbor Interaction Aware Graph Convolution Networks for Recommendation","display_name":"Neighbor Interaction Aware Graph Convolution Networks for Recommendation","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3035143414","doi":"https://doi.org/10.1145/3397271.3401123","mag":"3035143414"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401123","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101057614","display_name":"Jianing Sun","orcid":"https://orcid.org/0009-0001-4943-6038"},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jianing Sun","raw_affiliation_strings":["Huawei Noah's Ark Lab & Montreal Research Center, Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab & Montreal Research Center, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101906738","display_name":"Yingxue Zhang","orcid":"https://orcid.org/0000-0002-8370-3873"},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yingxue Zhang","raw_affiliation_strings":["Huawei Noah's Ark Lab & Montreal Research Center, Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab & Montreal Research Center, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648538","display_name":"Wei Guo","orcid":"https://orcid.org/0000-0001-8616-0221"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Guo","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019541869","display_name":"Huifeng Guo","orcid":"https://orcid.org/0000-0002-7393-8994"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huifeng Guo","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054330014","display_name":"Ruiming Tang","orcid":"https://orcid.org/0000-0002-9224-2431"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiming Tang","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083350101","display_name":"Xiuqiang He","orcid":"https://orcid.org/0000-0002-4115-8205"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuqiang He","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410908","display_name":"Chen Ma","orcid":"https://orcid.org/0000-0001-7933-9813"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chen Ma","raw_affiliation_strings":["McGill University, Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009031715","display_name":"Mark Coates","orcid":"https://orcid.org/0000-0001-5030-1379"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mark Coates","raw_affiliation_strings":["McGill University, Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I5023651"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":173,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1289","last_page":"1298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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":0.9937999844551086,"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/T11478","display_name":"Caching and Content Delivery","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7863036394119263},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7342410683631897},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.634743869304657},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6196393966674805},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5635069608688354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5196386575698853},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4888457655906677},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.48567354679107666},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4366450309753418},{"id":"https://openalex.org/keywords/movielens","display_name":"MovieLens","score":0.4322752058506012},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40215593576431274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3695894479751587},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.35260897874832153},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35064589977264404},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.34521111845970154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7863036394119263},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7342410683631897},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.634743869304657},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6196393966674805},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5635069608688354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5196386575698853},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4888457655906677},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.48567354679107666},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4366450309753418},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.4322752058506012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40215593576431274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3695894479751587},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35260897874832153},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35064589977264404},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.34521111845970154},{"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:10.1145/3397271.3401123","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.46000000834465027,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W46938732","https://openalex.org/W1533861849","https://openalex.org/W1994389483","https://openalex.org/W2049455633","https://openalex.org/W2054141820","https://openalex.org/W2136189984","https://openalex.org/W2140310134","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2519887557","https://openalex.org/W2604662567","https://openalex.org/W2605350416","https://openalex.org/W2624407581","https://openalex.org/W2624431344","https://openalex.org/W2773640334","https://openalex.org/W2783272285","https://openalex.org/W2785397276","https://openalex.org/W2793768763","https://openalex.org/W2807021761","https://openalex.org/W2888838693","https://openalex.org/W2894039884","https://openalex.org/W2906340703","https://openalex.org/W2908404712","https://openalex.org/W2914721378","https://openalex.org/W2945266622","https://openalex.org/W2945827670","https://openalex.org/W2951626319","https://openalex.org/W2955380732","https://openalex.org/W2962711740","https://openalex.org/W2963043672","https://openalex.org/W2963415211","https://openalex.org/W2963456618","https://openalex.org/W2963869731","https://openalex.org/W2964649670","https://openalex.org/W2972774416","https://openalex.org/W2997261254","https://openalex.org/W3003372423","https://openalex.org/W3098649723","https://openalex.org/W3099386565","https://openalex.org/W3100278010","https://openalex.org/W3100848837","https://openalex.org/W3102972033","https://openalex.org/W3104030692","https://openalex.org/W4297971002"],"related_works":["https://openalex.org/W2797500822","https://openalex.org/W2355698112","https://openalex.org/W2022984797","https://openalex.org/W2986679525","https://openalex.org/W2794458286","https://openalex.org/W4205822456","https://openalex.org/W4299358966","https://openalex.org/W2537367858","https://openalex.org/W2981634480","https://openalex.org/W4288082747"],"abstract_inverted_index":{"Personalized":[0],"recommendation":[1,220],"plays":[2],"an":[3,28,54],"important":[4],"role":[5],"in":[6,144,158,166,266,268,278],"many":[7,38],"online":[8,251],"services.":[9],"Substantial":[10],"research":[11],"has":[12],"been":[13],"dedicated":[14],"to":[15,22,56,60,67,93,130,153,215,255],"learning":[16],"embeddings":[17],"of":[18,34,71,190,243,270],"users":[19],"and":[20,50,77,113,147,185,264,272],"items":[21],"predict":[23],"a":[24,132,171,204,216,227,240,244,279],"user's":[25],"preference":[26],"for":[27,80,235],"item":[29,114],"based":[30],"on":[31,199,226],"the":[32,35,68,95,102,107,137,141,145,155,159,163,179,187,191,232,260],"similarity":[33],"representations.":[36],"In":[37,53],"settings,":[39],"there":[40],"is":[41,99],"abundant":[42],"relationship":[43],"information,":[44],"including":[45],"user-item":[46,96,192],"interaction":[47],"history,":[48],"user-user":[49],"item-item":[51],"similarities.":[52],"attempt":[55],"exploit":[57,186],"these":[58,83,148],"relationships":[59],"learn":[61],"better":[62],"embeddings,":[63],"researchers":[64],"have":[65,86],"turned":[66],"emerging":[69],"field":[70],"Graph":[72],"Convolutional":[73],"Neural":[74],"Networks":[75],"(GCNs),":[76],"applied":[78],"GCNs":[79,92,103],"recommendation.":[81],"Although":[82],"prior":[84],"works":[85],"demonstrated":[87],"promising":[88],"performance,":[89],"directly":[90],"apply":[91],"process":[94],"bipartite":[97,193],"graph":[98,119],"suboptimal":[100],"because":[101],"do":[104],"not":[105],"consider":[106],"intrinsic":[108],"differences":[109],"between":[110,182],"user":[111],"nodes":[112,143,184],"nodes.":[115],"Additionally,":[116],"existing":[117],"large-scale":[118,228],"neural":[120],"networks":[121],"use":[122],"aggregation":[123,150],"functions":[124],"such":[125],"as":[126,239],"sum/mean/max":[127],"pooling":[128],"operations":[129],"generate":[131],"node":[133],"embedding":[134],"that":[135,257],"considers":[136],"nodes'":[138],"neighborhood":[139],"(i.e.,":[140],"adjacent":[142],"graph),":[146],"simple":[149],"strategies":[151],"fail":[152],"preserve":[154],"relational":[156,180],"information":[157,181],"neighborhood.":[160],"To":[161],"resolve":[162],"above":[164],"limitations,":[165],"this":[167],"paper,":[168],"we":[169,211],"propose":[170],"novel":[172],"framework":[173,214],"NIA-GCN,":[174],"which":[175],"can":[176],"explicitly":[177],"model":[178],"neighbor":[183],"heterogeneous":[188],"nature":[189],"graph.":[194],"We":[195,222],"conduct":[196],"empirical":[197],"studies":[198],"four":[200],"public":[201],"benchmarks,":[202],"demonstrating":[203,231],"significant":[205,224],"improvement":[206,225],"over":[207],"state-of-the-art":[208],"approaches.":[209],"Furthermore,":[210,250],"generalize":[212],"our":[213,236],"commercial":[217,229,247],"App":[218,281],"store":[219],"scenario.":[221],"observe":[223],"dataset,":[230],"practical":[233],"potential":[234],"proposed":[237],"solution":[238],"key":[241],"component":[242],"large":[245],"scale":[246],"recommender":[248],"system.":[249],"experiments":[252],"are":[253],"conducted":[254],"demonstrate":[256],"NIA-GCN":[258],"outperforms":[259],"baseline":[261],"by":[262],"10.19%":[263],"9.95%":[265],"average":[267],"terms":[269],"CTR":[271],"CVR":[273],"during":[274],"ten-day":[275],"AB":[276],"test":[277],"mainstream":[280],"store.":[282]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":35},{"year":2023,"cited_by_count":45},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}