{"id":"https://openalex.org/W2911381491","doi":"https://doi.org/10.1109/bigdata.2018.8622235","title":"Research on User Consumption Behavior Prediction Based on Improved XGBoost Algorithm","display_name":"Research on User Consumption Behavior Prediction Based on Improved XGBoost Algorithm","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2911381491","doi":"https://doi.org/10.1109/bigdata.2018.8622235","mag":"2911381491"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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":null,"display_name":"Wang XingFen","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang XingFen","raw_affiliation_strings":["School of Information Management, Beijing Information Science and Technology University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100567288","display_name":"Xiangbin Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Xiangbin","raw_affiliation_strings":["School of Information Management, Beijing Information Science and Technology University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037007055","display_name":"Yangchun Ma","orcid":"https://orcid.org/0000-0001-7500-3423"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ma Yangchun","raw_affiliation_strings":["School of Information Management, Beijing Information Science and Technology University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":2.7037,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.92488546,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"4169","last_page":"4175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9855999946594238,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9855999946594238,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9812999963760376,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7081000208854675},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6324706077575684},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6320825815200806},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.608403205871582},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5396841168403625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48552608489990234},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.48230600357055664},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4747759997844696},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.47019824385643005},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45715612173080444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44499504566192627},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3858520984649658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7081000208854675},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6324706077575684},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6320825815200806},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.608403205871582},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5396841168403625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48552608489990234},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.48230600357055664},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4747759997844696},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.47019824385643005},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45715612173080444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44499504566192627},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3858520984649658},{"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:10.1109/bigdata.2018.8622235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2019177758","https://openalex.org/W2076618162","https://openalex.org/W2124756613","https://openalex.org/W4239647775","https://openalex.org/W6655415660"],"related_works":["https://openalex.org/W4367335967","https://openalex.org/W4224922629","https://openalex.org/W4396689258","https://openalex.org/W2964553505","https://openalex.org/W2280390767","https://openalex.org/W4386124392","https://openalex.org/W2106524649","https://openalex.org/W4372055074","https://openalex.org/W3176503453","https://openalex.org/W4388878213"],"abstract_inverted_index":{"This":[0],"paper":[1],"is":[2,33,162,171],"to":[3,20,35,40,76,111,118,139],"propose":[4],"an":[5,26],"improved":[6,69],"algorithm":[7,19,71,135],"in":[8,25,91],"modeling":[9],"user":[10,80],"consumption":[11],"behavior,":[12],"which":[13,56],"combined":[14],"Logistic":[15,131,157],"regression":[16,48,65,132,158],"and":[17,102,116,123,133,164],"XGBoost":[18,134,160],"predict":[21],"users'":[22],"purchasing":[23],"behavior":[24],"e-commerce":[27],"website.XGBoost,":[28],"as":[29,127],"a":[30],"feature":[31,54],"transformation,":[32],"used":[34,137,177],"make":[36],"sample":[37],"prediction.":[38],"According":[39],"the":[41,44,52,59,63,78,87,113,120,124,128,149,165,169],"prediction":[42],"results,":[43],"information":[45],"of":[46,62,148,151,168],"each":[47],"tree":[49],"will":[50,57,73],"construct":[51],"new":[53],"vector,":[55],"be":[58,74,112,119],"input":[60],"data":[61,99],"logistic":[64],"model.":[66],"The":[67,153],"previous":[68],"clustering":[70],"[1]":[72],"involved":[75],"cluster":[77],"different":[79],"divisions":[81],"for":[82,104],"further":[83],"comparative":[84],"analysis":[85],"with":[86],"three":[88],"predictive":[89],"models":[90,143],"this":[92],"paper.Specifically,":[93],"more":[94],"than":[95,173],"50":[96],"million":[97],"original":[98],"are":[100,108,136],"collected":[101],"preprocessed":[103],"correlation":[105],"mining.":[106],"60%":[107],"selected":[109],"randomly":[110],"training":[114],"set":[115,122,140],"20%":[117,126],"verification":[121],"rest":[125],"test":[129],"set.":[130],"respectively":[138],"up":[141],"two":[142],"based":[144],"on":[145,159],"making":[146],"use":[147],"advantages":[150],"each.":[152],"research":[154],"shows":[155],"that":[156],"method":[161],"feasible":[163],"evaluation":[166],"index":[167],"model":[170],"better":[172],"any":[174],"methods":[175],"being":[176],"alone.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
