{"id":"https://openalex.org/W2976462669","doi":"https://doi.org/10.1145/3308558.3313468","title":"Neural IR Meets Graph Embedding: A Ranking Model for Product Search","display_name":"Neural IR Meets Graph Embedding: A Ranking Model for Product Search","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2976462669","doi":"https://doi.org/10.1145/3308558.3313468","mag":"2976462669"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1901.08286","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100368731","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0002-7849-208X"},"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":"Yuan Zhang","raw_affiliation_strings":["Peking University, China"],"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023"},"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":"Dong Wang","raw_affiliation_strings":["Peking University, China"],"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100456324","display_name":"Yan Zhang","orcid":"https://orcid.org/0000-0002-8322-467X"},"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":"Yan Zhang","raw_affiliation_strings":["Peking University, China"],"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.672,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":46,"citation_normalized_percentile":{"value":0.999907,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2390","last_page":"2400"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Graph Neural Network Models and Applications","score":0.9998,"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/T11273","display_name":"Graph Neural Network Models and Applications","score":0.9998,"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/T10627","display_name":"Image Feature Retrieval and Recognition Techniques","score":0.998,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":"Natural Language Processing","score":0.9936,"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/leverage","display_name":"Leverage (statistics)","score":0.6457168},{"id":"https://openalex.org/keywords/knowledge-graph-embedding","display_name":"Knowledge Graph Embedding","score":0.632359},{"id":"https://openalex.org/keywords/feature-matching","display_name":"Feature Matching","score":0.608242},{"id":"https://openalex.org/keywords/network-embedding","display_name":"Network Embedding","score":0.59741},{"id":"https://openalex.org/keywords/signal-processing-on-graphs","display_name":"Signal Processing on Graphs","score":0.577368},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image Retrieval","score":0.57239},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.48847},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.43844542}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8201658},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7449875},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6457168},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.605358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5173616},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.50585175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49717644},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.48847},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.43844542},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40663624},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35766196},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.2621696},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24429998},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1901.08286","pdf_url":"https://arxiv.org/pdf/1901.08286","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1901.08286","pdf_url":"https://arxiv.org/pdf/1901.08286","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":54,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1678356000","https://openalex.org/W1854214752","https://openalex.org/W1888005072","https://openalex.org/W1972594981","https://openalex.org/W1981485659","https://openalex.org/W2021503317","https://openalex.org/W2036743095","https://openalex.org/W2061296752","https://openalex.org/W2062364080","https://openalex.org/W2097443371","https://openalex.org/W2117831564","https://openalex.org/W2122901787","https://openalex.org/W2136189984","https://openalex.org/W2138621811","https://openalex.org/W2147152072","https://openalex.org/W2155482025","https://openalex.org/W2160555926","https://openalex.org/W2163375626","https://openalex.org/W2164173709","https://openalex.org/W2166743161","https://openalex.org/W2170738476","https://openalex.org/W2186845332","https://openalex.org/W2191333630","https://openalex.org/W2336445533","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2536015822","https://openalex.org/W2539671052","https://openalex.org/W2610153490","https://openalex.org/W2611099133","https://openalex.org/W2612872092","https://openalex.org/W2613589950","https://openalex.org/W2624407581","https://openalex.org/W2626505907","https://openalex.org/W2737092125","https://openalex.org/W2740070748","https://openalex.org/W2741497758","https://openalex.org/W2747122324","https://openalex.org/W2798988488","https://openalex.org/W2949989304","https://openalex.org/W2950133940","https://openalex.org/W2950572117","https://openalex.org/W2963043672","https://openalex.org/W2963157366","https://openalex.org/W2963421945","https://openalex.org/W2963460103","https://openalex.org/W2964321699","https://openalex.org/W3099984837","https://openalex.org/W3100848837","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4252076394","https://openalex.org/W4293651439"],"related_works":["https://openalex.org/W3206528106","https://openalex.org/W3038102983","https://openalex.org/W3036264823","https://openalex.org/W2950907416","https://openalex.org/W2932872266","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W2082479932","https://openalex.org/W1559483280"],"abstract_inverted_index":{"Recently,":[0],"neural":[1,42,58],"models":[2,60],"for":[3,14,65],"information":[4,89],"retrieval":[5,59,117],"are":[6],"becoming":[7],"increasingly":[8],"popular.":[9],"They":[10],"provide":[11],"effective":[12],"approaches":[13],"product":[15],"search":[16,92],"due":[17],"to":[18,29,56,61,76,90],"their":[19],"competitive":[20],"advantages":[21],"in":[22,37,40,52,124],"semantic":[23],"matching.":[24],"However,":[25],"it":[26],"is":[27],"challenging":[28],"use":[30],"graph-based":[31],"features,":[32],"though":[33],"proved":[34],"very":[35],"useful":[36],"IR":[38],"literature,":[39],"these":[41],"approaches.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47],"leverage":[48],"the":[49,78],"recent":[50],"advances":[51],"graph":[53],"embedding":[54],"techniques":[55],"enable":[57],"exploit":[62],"graph-structured":[63],"data":[64],"automatic":[66],"feature":[67,122],"extraction.":[68],"The":[69],"proposed":[70,107],"approach":[71,108],"can":[72],"not":[73],"only":[74],"help":[75],"overcome":[77],"long-tail":[79],"problem":[80],"of":[81],"click-through":[82],"data,":[83],"but":[84],"also":[85],"incorporate":[86],"external":[87],"heterogeneous":[88],"improve":[91],"results.":[93],"Extensive":[94],"experiments":[95],"on":[96],"a":[97,121],"real-world":[98],"e-commerce":[99],"dataset":[100],"demonstrate":[101],"significant":[102],"improvement":[103],"achieved":[104],"by":[105],"our":[106],"over":[109],"multiple":[110],"strong":[111],"baselines":[112],"both":[113],"as":[114,120],"an":[115],"individual":[116],"model":[118],"and":[119],"used":[123],"learning-to-rank":[125],"frameworks.":[126]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2976462669","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2024-11-23T16:57:50.725332","created_date":"2019-10-03"}