{"id":"https://openalex.org/W3137020683","doi":"https://doi.org/10.1109/bigdata50022.2020.9378378","title":"Recommendations of Compatible Accessories in e-Commerce","display_name":"Recommendations of Compatible Accessories in e-Commerce","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137020683","doi":"https://doi.org/10.1109/bigdata50022.2020.9378378","mag":"3137020683"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5048670727","display_name":"San He We","orcid":null},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"San He We","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049719821","display_name":"Unaiza Ahsan","orcid":null},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Unaiza Ahsan","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090308024","display_name":"Mingming Guo","orcid":"https://orcid.org/0000-0002-5456-8027"},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingming Guo","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047653779","display_name":"Simon Hughes","orcid":"https://orcid.org/0000-0002-7923-3506"},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Simon Hughes","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102967365","display_name":"Xiquan Cui","orcid":"https://orcid.org/0009-0005-5306-8839"},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiquan Cui","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040734008","display_name":"Khalifeh Al Jadda","orcid":null},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khalifeh Al Jadda","raw_affiliation_strings":["The Home Depot, Atlanta, U.S"],"affiliations":[{"raw_affiliation_string":"The Home Depot, Atlanta, U.S","institution_ids":["https://openalex.org/I2799939184"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5048670727"],"corresponding_institution_ids":["https://openalex.org/I2799939184"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21475448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":null,"first_page":"5296","last_page":"5304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T11550","display_name":"Text and Document Classification Technologies","score":0.9995999932289124,"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":0.9984999895095825,"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/compatibility","display_name":"Compatibility (geochemistry)","score":0.7566245794296265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.705784022808075},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.675665557384491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239370465278625},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.49810147285461426},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4976356327533722},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4283914864063263},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4217490553855896},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3960287272930145},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3630009889602661},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3371117115020752},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12668827176094055}],"concepts":[{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.7566245794296265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705784022808075},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.675665557384491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239370465278625},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.49810147285461426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4976356327533722},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4283914864063263},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4217490553855896},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3960287272930145},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3630009889602661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3371117115020752},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12668827176094055},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C42360764","wikidata":"https://www.wikidata.org/wiki/Q83588","display_name":"Chemical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1647671624","https://openalex.org/W1880262756","https://openalex.org/W1991418309","https://openalex.org/W2014687456","https://openalex.org/W2064675550","https://openalex.org/W2153579005","https://openalex.org/W2157331557","https://openalex.org/W2171590421","https://openalex.org/W2221507685","https://openalex.org/W2295598076","https://openalex.org/W2560298007","https://openalex.org/W2562607067","https://openalex.org/W2620212701","https://openalex.org/W2734096527","https://openalex.org/W2737102415","https://openalex.org/W2768348081","https://openalex.org/W2795117763","https://openalex.org/W2892485145","https://openalex.org/W2892766675","https://openalex.org/W2896962583","https://openalex.org/W2907496897","https://openalex.org/W2911964244","https://openalex.org/W2945827670","https://openalex.org/W2949860550","https://openalex.org/W2951587203","https://openalex.org/W2963403868","https://openalex.org/W2963572510","https://openalex.org/W2964213740","https://openalex.org/W2970294686","https://openalex.org/W3099462466","https://openalex.org/W3100278010","https://openalex.org/W3102476541","https://openalex.org/W3122507327","https://openalex.org/W4231510805","https://openalex.org/W4285719527","https://openalex.org/W4288083766","https://openalex.org/W4294170691","https://openalex.org/W4385245566","https://openalex.org/W4394649266","https://openalex.org/W6636915900","https://openalex.org/W6639619044","https://openalex.org/W6682691769","https://openalex.org/W6738813834","https://openalex.org/W6739901393","https://openalex.org/W6740667046","https://openalex.org/W6746999808","https://openalex.org/W6754617643","https://openalex.org/W6864334237"],"related_works":["https://openalex.org/W2390255551","https://openalex.org/W2367511445","https://openalex.org/W2381930792","https://openalex.org/W2358901819","https://openalex.org/W745733672","https://openalex.org/W2983545107","https://openalex.org/W2389102290","https://openalex.org/W2094745766","https://openalex.org/W2358294942","https://openalex.org/W4367460280"],"abstract_inverted_index":{"We":[0,50],"address":[1],"the":[2,16,73,83,88,102],"problem":[3],"of":[4,18,62,87,104],"learning":[5,57],"how":[6],"compatible":[7],"two":[8],"products":[9],"are.":[10],"Assessing":[11],"compatibility":[12,19,66],"is":[13],"challenging":[14],"because":[15],"meaning":[17],"changes":[20],"depending":[21],"on":[22,111],"product":[23,45,48,70],"categories.":[24,71],"In":[25,97],"this":[26],"study,":[27],"we":[28,39,99],"leverage":[29],"domain":[30,124],"experts'":[31],"knowledge":[32],"to":[33,64],"generate":[34],"labels":[35,121],"and":[36,47,55,85],"datasets.":[37],"Next,":[38],"engineer":[40],"58":[41],"different":[42,60],"features":[43,63],"from":[44,91,123],"titles":[46],"descriptions.":[49],"experiment":[51],"with":[52],"both":[53],"tree-based":[54],"deep":[56],"classifiers":[58,105,116],"using":[59],"sets":[61],"capture":[65],"patterns":[67],"across":[68,80],"four":[69],"Although":[72],"performance":[74,103,119],"does":[75],"not":[76],"show":[77],"consistent":[78],"pattern":[79],"all":[81],"categories,":[82],"precision":[84],"recall":[86],"best":[89],"algorithm":[90],"most":[92],"categories":[93],"are":[94,106],"above":[95],"90%.":[96],"addition,":[98],"find":[100],"that":[101],"in":[107],"general":[108],"satisfactory.":[109],"Based":[110],"human":[112],"validation,":[113],"few":[114],"best-performing":[115],"demonstrate":[117],"better":[118],"than":[120],"generated":[122],"experts.":[125]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
