{"id":"https://openalex.org/W2951570486","doi":"https://doi.org/10.1145/3292500.3330790","title":"Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics","display_name":"Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2951570486","doi":"https://doi.org/10.1145/3292500.3330790","mag":"2951570486"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330790","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330790","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330790","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/10.1145/3292500.3330790","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091518548","display_name":"Chao Huang","orcid":"https://orcid.org/0009-0003-3740-4500"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]},{"id":"https://openalex.org/I72427458","display_name":"JDSU (United States)","ror":"https://ror.org/01a5v8x09","country_code":"US","type":"company","lineage":["https://openalex.org/I72427458"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chao Huang","raw_affiliation_strings":["University of Notre Dame & JD Digits, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame & JD Digits, Mountain View, CA, USA","institution_ids":["https://openalex.org/I72427458","https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100352416","display_name":"Xian Wu","orcid":"https://orcid.org/0000-0003-0840-5857"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xian Wu","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101905948","display_name":"Xuchao Zhang","orcid":"https://orcid.org/0000-0001-5344-456X"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuchao Zhang","raw_affiliation_strings":["Virginia Tech, Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022275632","display_name":"Chuxu Zhang","orcid":"https://orcid.org/0000-0002-8349-7926"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuxu Zhang","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101173099","display_name":"Jiashu Zhao","orcid":"https://orcid.org/0009-0000-7974-0156"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashu Zhao","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101771060","display_name":"Dawei Yin","orcid":"https://orcid.org/0000-0002-0684-6205"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Yin","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068157871","display_name":"Nitesh V. Chawla","orcid":"https://orcid.org/0000-0003-3932-5956"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nitesh V. Chawla","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5091518548"],"corresponding_institution_ids":["https://openalex.org/I107639228","https://openalex.org/I72427458"],"apc_list":null,"apc_paid":null,"fwci":21.3664,"has_fulltext":true,"cited_by_count":95,"citation_normalized_percentile":{"value":0.99536847,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2613","last_page":"2622"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9966999888420105,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9966999888420105,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9876999855041504,"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/computer-science","display_name":"Computer science","score":0.8342111110687256},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7216267585754395},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5885857343673706},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.580413818359375},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5463157892227173},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.49650126695632935},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4823243319988251},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4679611623287201},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4599335491657257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4539860785007477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44597601890563965},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.4338664412498474},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4098997414112091},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3013211190700531},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2916845679283142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8342111110687256},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7216267585754395},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5885857343673706},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.580413818359375},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5463157892227173},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.49650126695632935},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4823243319988251},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4679611623287201},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4599335491657257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4539860785007477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44597601890563965},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.4338664412498474},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4098997414112091},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3013211190700531},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2916845679283142},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330790","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330790","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330790","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330790","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330790","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330790","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3142689400","display_name":null,"funder_award_id":"IIS-1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4029481580","display_name":null,"funder_award_id":"1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G636084608","display_name":null,"funder_award_id":"No. IIS-1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2951570486.pdf","grobid_xml":"https://content.openalex.org/works/W2951570486.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W63326460","https://openalex.org/W1838102683","https://openalex.org/W1982978808","https://openalex.org/W2047332899","https://openalex.org/W2129129488","https://openalex.org/W2134560790","https://openalex.org/W2143403793","https://openalex.org/W2288074780","https://openalex.org/W2494566063","https://openalex.org/W2583674722","https://openalex.org/W2604847698","https://openalex.org/W2613328025","https://openalex.org/W2624190409","https://openalex.org/W2690721124","https://openalex.org/W2740098507","https://openalex.org/W2744939564","https://openalex.org/W2782696945","https://openalex.org/W2783118243","https://openalex.org/W2783272285","https://openalex.org/W2788663865","https://openalex.org/W2793768763","https://openalex.org/W2798283910","https://openalex.org/W2808955427","https://openalex.org/W2809112621","https://openalex.org/W2809280072","https://openalex.org/W2809503262","https://openalex.org/W2809583854","https://openalex.org/W2825179178","https://openalex.org/W2884134047","https://openalex.org/W2888983470","https://openalex.org/W2895806569","https://openalex.org/W2896962583","https://openalex.org/W2897825916","https://openalex.org/W2902040508","https://openalex.org/W2911535719","https://openalex.org/W2914231700","https://openalex.org/W2914971589","https://openalex.org/W2949274928","https://openalex.org/W2952042565","https://openalex.org/W2953118818","https://openalex.org/W2963323306","https://openalex.org/W2963367478","https://openalex.org/W3099136959","https://openalex.org/W3101830194","https://openalex.org/W3104030692"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2988126442","https://openalex.org/W4386799044","https://openalex.org/W2773208253","https://openalex.org/W4225394202","https://openalex.org/W1974414866","https://openalex.org/W4298287631"],"abstract_inverted_index":{"Online":[0],"purchase":[1,103],"forecasting":[2],"is":[3,11,29,34,145],"of":[4,14,135,165],"great":[5],"importance":[6,134],"in":[7],"e-commerce":[8,159],"platforms,":[9],"which":[10,94],"the":[12,41,97,114,133,162],"basis":[13],"how":[15],"to":[16,22,65,112,130,147],"present":[17],"personalized":[18],"interesting":[19],"product":[20,81],"lists":[21],"individual":[23],"customers.":[24],"However,":[25],"predicting":[26],"online":[27],"purchases":[28],"not":[30],"trivial":[31],"as":[32],"it":[33],"influenced":[35],"by":[36],"many":[37],"factors":[38],"including:":[39],"(i)":[40],"complex":[42,150],"temporal":[43,75,100,116],"pattern":[44,117],"with":[45,72],"hierarchical":[46,99],"inter-correlations;":[47],"(ii)":[48],"arbitrary":[49,78],"category":[50],"dependencies.":[51],"To":[52],"address":[53],"these":[54],"factors,":[55],"we":[56,85,106,123],"develop":[57,124],"a":[58,88,125,140],"Graph":[59],"Multi-Scale":[60],"Pyramid":[61],"Networks":[62],"(GMP)":[63],"framework":[64],"fully":[66],"exploit":[67],"users'":[68,102],"latent":[69],"behavioral":[70],"patterns":[71],"both":[73],"multi-scale":[74,89],"dynamics":[76],"and":[77],"inter-dependencies":[79],"among":[80,152],"categories.":[82],"In":[83],"GMP,":[84],"first":[86],"design":[87],"pyramid":[90],"modulation":[91],"network":[92,111,143],"architecture":[93],"seamlessly":[95],"preserves":[96],"underlying":[98],"factors--governing":[101],"behaviors.":[104],"Then,":[105],"employ":[107],"convolution":[108],"recurrent":[109],"neural":[110,142],"encode":[113],"categorical":[115],"at":[118],"each":[119,136],"scale.":[120],"After":[121],"that,":[122],"resolution-wise":[126],"recalibration":[127],"gating":[128],"mechanism":[129],"automatically":[131],"re-weight":[132],"scale-view":[137],"representations.":[138],"Finally,":[139],"context-graph":[141],"module":[144],"proposed":[146],"adaptively":[148],"uncover":[149],"dependencies":[151],"category-specific":[153],"purchases.":[154],"Extensive":[155],"experiments":[156],"on":[157],"real-world":[158],"datasets":[160],"demonstrate":[161],"superior":[163],"performance":[164],"our":[166],"method":[167],"over":[168],"state-of-the-art":[169],"baselines":[170],"across":[171],"various":[172],"settings.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}