{"id":"https://openalex.org/W4387846687","doi":"https://doi.org/10.1145/3583780.3615472","title":"Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation","display_name":"Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846687","doi":"https://doi.org/10.1145/3583780.3615472"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615472","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615472","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615472","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615472","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007722983","display_name":"Boxin Du","orcid":"https://orcid.org/0000-0002-1300-6140"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Boxin Du","raw_affiliation_strings":["Amazon, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021733716","display_name":"Rob Barton","orcid":"https://orcid.org/0000-0001-8062-4224"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rob Barton","raw_affiliation_strings":["Amazon, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009886816","display_name":"Grant S. Galloway","orcid":"https://orcid.org/0000-0003-2861-6504"},"institutions":[{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Grant Galloway","raw_affiliation_strings":["Amazon, Edinburgh, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Amazon, Edinburgh, United Kingdom","institution_ids":["https://openalex.org/I4210123934"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["Amazon, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057555460","display_name":"Shioulin Sam","orcid":"https://orcid.org/0009-0004-1223-5912"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shioulin Sam","raw_affiliation_strings":["Amazon, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073502763","display_name":"Ismail Tutar","orcid":"https://orcid.org/0009-0004-8369-4825"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ismail Tutar","raw_affiliation_strings":["Amazon, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066611124","display_name":"Changhe Yuan","orcid":"https://orcid.org/0000-0001-5268-6620"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Changhe Yuan","raw_affiliation_strings":["Amazon, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5007722983"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.1748,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56909942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4545","last_page":"4551"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9901999831199646,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9894999861717224,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7935106158256531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759962797164917},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7414582967758179},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.648706316947937},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6267399191856384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5242193937301636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5084859132766724},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4872657060623169},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4140554666519165},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30426353216171265}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7935106158256531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759962797164917},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7414582967758179},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.648706316947937},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6267399191856384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5242193937301636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5084859132766724},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4872657060623169},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4140554666519165},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30426353216171265}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615472","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615472","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615472","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"id":"doi:10.1145/3583780.3615472","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615472","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615472","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387846687.pdf","grobid_xml":"https://content.openalex.org/works/W4387846687.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1603598191","https://openalex.org/W2046804949","https://openalex.org/W2470394683","https://openalex.org/W2558748708","https://openalex.org/W2604314403","https://openalex.org/W2954691982","https://openalex.org/W2965857891","https://openalex.org/W2989532156","https://openalex.org/W3003451723","https://openalex.org/W3004507689","https://openalex.org/W3036446966","https://openalex.org/W3099152386","https://openalex.org/W3103513278","https://openalex.org/W3152507776","https://openalex.org/W3162114213","https://openalex.org/W3163416963","https://openalex.org/W3169799753","https://openalex.org/W3193631128","https://openalex.org/W3212385273","https://openalex.org/W4285261975","https://openalex.org/W6755573351"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W1692008701","https://openalex.org/W2597588799","https://openalex.org/W4360593462","https://openalex.org/W2562400057"],"abstract_inverted_index":{"Traditionally,":[0],"catalog":[1,48,108,128,170],"relationship":[2,49,63,117,129,175],"problems":[3],"in":[4,31,81,106,123,168],"e-commerce":[5],"stores":[6],"have":[7],"been":[8],"handled":[9],"as":[10],"pairwise":[11,100,113,151],"classification":[12],"tasks,":[13],"which":[14],"limit":[15],"the":[16,25,32,75,78,93,107,126,137,144,163],"ability":[17],"of":[18,77,125,149,165],"machine":[19],"learning":[20],"models":[21],"to":[22,56,70,73,91,142,155],"learn":[23],"from":[24,53,61],"diverse":[26,62],"relationships":[27],"among":[28],"different":[29],"entities":[30],"catalog.":[33],"In":[34],"this":[35,166],"paper,":[36],"we":[37],"leverage":[38,74],"heterogeneous":[39],"graphs":[40,60],"and":[41,66,134,157,173],"Graph":[42],"Neural":[43],"Networks":[44],"(GNNs)":[45],"for":[46,103,115,179],"improving":[47],"inference.":[50,118],"We":[51,85],"start":[52],"investigating":[54],"how":[55,69],"create":[57],"multi-entity,":[58],"multi-relationship":[59],"data":[64],"sources,":[65],"then":[67],"explore":[68],"utilizing":[71],"GNNs":[72,97],"knowledge":[76,94],"constructed":[79],"graph":[80],"a":[82,88,99,150],"self-supervised":[83],"fashion.":[84],"finally":[86],"propose":[87],"distillation":[89],"approach":[90,167],"transfer":[92],"learned":[95],"by":[96,153],"into":[98],"neural":[101],"network":[102],"seamless":[104],"deployment":[105],"pipeline":[109],"that":[110,122],"relies":[111],"on":[112],"input":[114],"inductive":[116],"Our":[119,160],"experiments":[120],"exhibit":[121],"two":[124],"representative":[127],"problems,":[130],"Title":[131],"Authority/Contributor":[132],"Authority":[133],"Broken":[135],"Variation,":[136],"proposed":[138],"framework":[139],"is":[140],"able":[141],"improve":[143],"recall":[145],"at":[146],"95%":[147],"precision":[148],"baseline":[152],"up":[154],"33.6%":[156],"14.0%,":[158],"respectively.":[159],"findings":[161],"highlight":[162],"effectiveness":[164],"advancing":[169],"quality":[171],"maintenance":[172],"accurate":[174],"modeling,":[176],"with":[177],"potential":[178],"broader":[180],"industry":[181],"adoption.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
