{"id":"https://openalex.org/W4387848686","doi":"https://doi.org/10.1145/3583780.3615035","title":"Retrieving GNN Architecture for Collaborative Filtering","display_name":"Retrieving GNN Architecture for Collaborative Filtering","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848686","doi":"https://doi.org/10.1145/3583780.3615035"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615035","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and 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/A5007455166","display_name":"Fengqi Liang","orcid":"https://orcid.org/0009-0002-6382-6486"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengqi Liang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101478660","display_name":"Huan Zhao","orcid":"https://orcid.org/0000-0002-0320-8718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huan Zhao","raw_affiliation_strings":["4Paradigm, Beijing, China"],"affiliations":[{"raw_affiliation_string":"4Paradigm, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020540821","display_name":"Zhenyi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyi Wang","raw_affiliation_strings":["Beijing University of Potsts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Potsts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051855078","display_name":"Wei Fang","orcid":"https://orcid.org/0000-0001-5618-0010"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Fang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100705849","display_name":"Chuan Shi","orcid":"https://orcid.org/0000-0002-3734-0266"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Shi","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5007455166"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.359,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85773898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1379","last_page":"1388"},"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":0.994700014591217,"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":0.9876999855041504,"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":0.8683794140815735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6060146689414978},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.582190215587616},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5696772933006287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5516817569732666},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5407495498657227},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.519199013710022},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48284631967544556},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.45558294653892517},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4533863961696625},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3746546506881714},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3517443537712097},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.2630830407142639}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8683794140815735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6060146689414978},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.582190215587616},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5696772933006287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5516817569732666},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5407495498657227},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.519199013710022},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48284631967544556},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.45558294653892517},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4533863961696625},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3746546506881714},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3517443537712097},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2630830407142639},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615035","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1108969597","display_name":null,"funder_award_id":"62192784","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1343331596","display_name":null,"funder_award_id":"62002029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"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/G3290335512","display_name":null,"funder_award_id":"62192784, U1936104, U20B2045, 62172052, 62002029","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/G5576251551","display_name":null,"funder_award_id":"62172052","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5620609558","display_name":null,"funder_award_id":"U1936104","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/G6424503130","display_name":null,"funder_award_id":"U20B204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8003444953","display_name":null,"funder_award_id":"U20B2045","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1994389483","https://openalex.org/W2016666840","https://openalex.org/W2042281163","https://openalex.org/W2072112759","https://openalex.org/W2086825382","https://openalex.org/W2091158010","https://openalex.org/W2544587078","https://openalex.org/W2596142952","https://openalex.org/W2605350416","https://openalex.org/W2793350103","https://openalex.org/W2807021761","https://openalex.org/W2945827670","https://openalex.org/W2964081807","https://openalex.org/W3012731857","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3088777230","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3100848837","https://openalex.org/W3167990860","https://openalex.org/W3176189116","https://openalex.org/W3195040486","https://openalex.org/W4206929635","https://openalex.org/W4212799635","https://openalex.org/W4212843742","https://openalex.org/W4226204438","https://openalex.org/W4321392501"],"related_works":["https://openalex.org/W2398165842","https://openalex.org/W197922276","https://openalex.org/W4226158052","https://openalex.org/W1809731386","https://openalex.org/W4388863299","https://openalex.org/W2160332782","https://openalex.org/W2945818642","https://openalex.org/W2146181690","https://openalex.org/W2006998619","https://openalex.org/W2556985004"],"abstract_inverted_index":{"Graph":[0],"Neural":[1,31],"Networks":[2],"(GNNs)":[3],"have":[4],"been":[5],"widely":[6],"used":[7],"in":[8,46,57,102,149],"Collaborative":[9],"Filtering":[10],"(CF).":[11],"However,":[12],"when":[13,77],"given":[14,113],"a":[15,37,61,70,103,110,114,171],"new":[16,79,115,147,181],"recommendation":[17],"scenario,":[18],"the":[19,55,84,156,179],"current":[20],"options":[21],"are":[22,44,186],"either":[23,140],"selecting":[24],"from":[25],"existing":[26],"GNN":[27,39],"architectures":[28],"or":[29,51,143],"employing":[30],"Architecture":[32],"Search":[33],"(NAS)":[34],"to":[35,68],"obtain":[36],"well-performing":[38,71,111],"model,":[40],"both":[41],"of":[42,48,151],"which":[43],"expensive":[45],"terms":[47,150],"human":[49],"expertise":[50],"computational":[52],"resources.To":[53],"address":[54],"problem,":[56],"this":[58],"work,we":[59],"propose":[60],"novel":[62],"neural":[63,85],"retrieval":[64,86,104],"approach,":[65],"dubbed":[66],"RGCF,":[67],"search":[69],"architecture":[72,112],"for":[73],"GNN-based":[74],"CF":[75],"rapidly":[76],"handling":[78],"scenarios.":[80],"Specifically,":[81],"we":[82],"design":[83],"approach":[87,177],"based":[88,175],"on":[89,124,145,178],"meta-learning":[90],"by":[91,141],"developing":[92],"two-level":[93],"meta-features,":[94],"ranking":[95],"loss,":[96],"and":[97,101,131,153,184],"task-level":[98],"data":[99,185],"augmentation,":[100],"paradigm,":[105],"RGCF":[106,136,166],"can":[107],"directly":[108],"return":[109],"dataset":[116],"(query),":[117],"thus":[118],"being":[119],"efficient":[120],"inherently.":[121],"Experimental":[122],"results":[123],"two":[125,146,180],"mainstream":[126],"tasks,":[127],"i.e.,":[128],"rating":[129],"prediction":[130],"item":[132],"ranking,":[133],"show":[134],"that":[135,165],"outperforms":[137],"all":[138],"models":[139],"human-designed":[142],"NAS":[144,176],"datasets":[148],"effectiveness":[152],"efficiency.":[154],"Particularly,":[155],"efficiency":[157],"improvement":[158],"is":[159,167],"significant,":[160],"taking":[161],"as":[162],"an":[163],"example":[164],"61.7-206.3x":[168],"faster":[169],"than":[170],"typical":[172],"reinforcement":[173],"learning":[174],"datasets.":[182],"Code":[183],"available":[187],"at":[188],"https://github.com/BUPT-GAMMA/RGCF.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
