{"id":"https://openalex.org/W2090640121","doi":"https://doi.org/10.1145/1014052.1014138","title":"Identifying early buyers from purchase data","display_name":"Identifying early buyers from purchase data","publication_year":2004,"publication_date":"2004-08-22","ids":{"openalex":"https://openalex.org/W2090640121","doi":"https://doi.org/10.1145/1014052.1014138","mag":"2090640121"},"language":"en","primary_location":{"id":"doi:10.1145/1014052.1014138","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5041224387","display_name":"Paat Rusmevichientong","orcid":"https://orcid.org/0000-0001-9584-4203"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Paat Rusmevichientong","raw_affiliation_strings":["Cornell University, Ithaca, NY","Cornell University (Ithaca, NY);"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Cornell University (Ithaca, NY);","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103861122","display_name":"Shenghuo Zhu","orcid":null},"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":"Shenghuo Zhu","raw_affiliation_strings":["Amazon.com, Seattle, WA","Amazon.com, Seattle, WA#TAB#"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Seattle, WA","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon.com, Seattle, WA#TAB#","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090104919","display_name":"David A. Selinger","orcid":null},"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":"David Selinger","raw_affiliation_strings":["Amazon.com, Seattle, WA","Amazon.com, Seattle, WA#TAB#"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Seattle, WA","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon.com, Seattle, WA#TAB#","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041224387"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.868,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.7717947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"671","last_page":"677"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12659","display_name":"Innovation Diffusion and Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12659","display_name":"Innovation Diffusion and Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.592125415802002},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.3279435932636261}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.592125415802002},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.3279435932636261}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1014052.1014138","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1985123706","https://openalex.org/W2042123098","https://openalex.org/W2048392812","https://openalex.org/W2056609785","https://openalex.org/W2069860303","https://openalex.org/W2090008771","https://openalex.org/W2090254306","https://openalex.org/W2265720734","https://openalex.org/W4230562141","https://openalex.org/W4252507658"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Market":[0],"research":[1],"has":[2],"shown":[3],"that":[4],"consumers":[5,78,85,95],"exhibit":[6],"a":[7,41,70,110,132,140],"variety":[8],"of":[9,47,63,108,112,123,134],"different":[10],"purchasing":[11],"behaviors;":[12],"specifically,":[13],"some":[14],"tend":[15],"to":[16,44,77,83,105,167],"purchase":[17,52,61,96,169],"products":[18,97],"earlier":[19,98],"than":[20,99],"other":[21,100],"consumers.":[22],"Identifying":[23],"such":[24],"early":[25,49,102],"buyers":[26,50,103],"can":[27],"help":[28],"personalize":[29],"marketing":[30],"strategies,":[31],"potentially":[32],"improving":[33],"their":[34],"effectiveness.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39,68],"present":[40],"non-parametric":[42],"approach":[43],"the":[45,59,89,106,115,121,124,135,165],"problem":[46,107,130,138],"identifying":[48],"from":[51,171],"data.":[53],"Our":[54],"formulation":[55],"takes":[56],"as":[57],"inputs":[58],"detailed":[60],"information":[62],"each":[64],"consumer,":[65],"with":[66,117],"which":[67],"construct":[69],"weighted":[71],"directed":[72,141],"graph":[73,116],"whose":[74,80],"nodes":[75,113],"correspond":[76,82],"and":[79,126,157,159],"edges":[81],"purchases":[84],"have":[86],"in":[87,114,139],"common;":[88],"edge":[90],"weights":[91,122],"indicate":[92],"how":[93],"frequently":[94],"consumers.Identifying":[101],"corresponds":[104],"finding":[109],"subset":[111],"maximum":[118,136],"difference":[119],"between":[120],"outgoing":[125],"incoming":[127],"edges.":[128],"This":[129],"is":[131],"variation":[133],"cut":[137],"graph.":[142],"We":[143,163],"provide":[144],"an":[145],"approximation":[146],"algorithm":[147,166],"based":[148],"on":[149],"semidefinite":[150],"programming":[151],"(SDP)":[152],"relaxations":[153],"pioneered":[154],"by":[155],"Goemans":[156],"Williamson,":[158],"analyze":[160],"its":[161],"performance.":[162],"apply":[164],"real":[168],"data":[170],"Amazon.com,":[172],"providing":[173],"new":[174],"insights":[175],"into":[176],"consumer":[177],"behaviors.":[178]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}