{"id":"https://openalex.org/W2952990337","doi":"https://doi.org/10.1145/3292500.3330728","title":"Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework","display_name":"Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2952990337","doi":"https://doi.org/10.1145/3292500.3330728","mag":"2952990337"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330728","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330728","pdf_url":null,"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 &amp; 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/A5052162023","display_name":"Xiaoyang Ma","orcid":"https://orcid.org/0000-0001-7783-0609"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyang Ma","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322306","display_name":"Lan Zhang","orcid":"https://orcid.org/0000-0002-6905-2189"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lan Zhang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101467168","display_name":"Lan Xu","orcid":"https://orcid.org/0000-0003-0743-1965"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lan Xu","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745770","display_name":"Zhicheng Liu","orcid":"https://orcid.org/0000-0002-6278-1813"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Liu","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100359773","display_name":"Chen Ge","orcid":"https://orcid.org/0000-0002-8093-940X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Chen","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100626611","display_name":"Zhili Xiao","orcid":"https://orcid.org/0000-0002-0478-0716"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhili Xiao","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100699735","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0002-1534-8953"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["Tencent, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shanghai, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032693482","display_name":"Zhengtao Wu","orcid":"https://orcid.org/0000-0002-3305-7316"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengtao Wu","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5052162023"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":1.268,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.79700415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2403","last_page":"2411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9836999773979187,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8108972311019897},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6829969882965088},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5702201128005981},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.523784339427948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5220887064933777},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5214959383010864},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4944593906402588},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.45934754610061646},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45663052797317505},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44760650396347046},{"id":"https://openalex.org/keywords/online-advertising","display_name":"Online advertising","score":0.4137658178806305},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.3868641257286072},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35252541303634644},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10465726256370544}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8108972311019897},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6829969882965088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5702201128005981},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.523784339427948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5220887064933777},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5214959383010864},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4944593906402588},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.45934754610061646},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45663052797317505},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44760650396347046},{"id":"https://openalex.org/C512338625","wikidata":"https://www.wikidata.org/wiki/Q624902","display_name":"Online advertising","level":3,"score":0.4137658178806305},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.3868641257286072},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35252541303634644},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10465726256370544},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330728","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330728","pdf_url":null,"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 &amp; Data Mining","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":23,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1600744878","https://openalex.org/W2000942133","https://openalex.org/W2029171653","https://openalex.org/W2036455044","https://openalex.org/W2099733338","https://openalex.org/W2130681737","https://openalex.org/W2144685566","https://openalex.org/W2340354238","https://openalex.org/W2528639018","https://openalex.org/W2530386080","https://openalex.org/W2539781657","https://openalex.org/W2552480641","https://openalex.org/W2604906612","https://openalex.org/W2605070684","https://openalex.org/W2744043447","https://openalex.org/W2797032817","https://openalex.org/W2798056406","https://openalex.org/W2798058877","https://openalex.org/W2952042565","https://openalex.org/W2963858333","https://openalex.org/W2964182926","https://openalex.org/W4289256736"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W2795261237"],"abstract_inverted_index":{"Understanding":[0],"and":[1,39,142,155,181,212,217,247],"forecasting":[2],"user":[3,36,67,178],"visits":[4,68,179],"is":[5,18,59,146,248],"of":[6,12,20,46,48,66,79,94,113,150,219],"great":[7],"importance":[8],"for":[9,26,69,87,106,123,130,158,195],"a":[10,92,148,173,183,192],"variety":[11],"tasks,":[13],"e.g.,":[14],"online":[15,53,207],"advertising,":[16],"which":[17,190],"one":[19],"the":[21,64,202,215,220,244],"most":[22,112],"profitable":[23],"business":[24,132],"models":[25,95,137],"Internet":[27],"services.":[28],"Publishers":[29],"sell":[30],"advertising":[31,54,210],"spaces":[32],"in":[33,51,127,168,205,222,237],"advance":[34],"with":[35,253],"visit":[37],"volume":[38],"attributes":[40],"guarantees.":[41],"There":[42,145],"are":[43,84,121],"usually":[44,138],"tens":[45],"thousands":[47],"attribute":[49,71,118],"combinations":[50],"an":[52,151],"system.":[55],"The":[56,228],"key":[57],"problem":[58],"how":[60],"to":[61,250,255],"accurately":[62],"forecast":[63],"number":[65,93,246],"each":[70],"combination.":[72],"Many":[73],"traditional":[74],"work":[75],"characterizing":[76],"temporal":[77],"trends":[78],"every":[80],"single":[81],"time":[82,89,108,141,161,197],"series":[83,109,162,198],"quite":[85],"inefficient":[86],"large-scale":[88,177,225],"series.":[90],"Recently,":[91],"based":[96],"on":[97,176],"deep":[98,135,185],"learning":[99,136],"or":[100,120],"matrix":[101],"factorization":[102,188],"have":[103],"been":[104],"proposed":[105,203],"high-dimensional":[107,160,196],"forecasting.":[110,163,199],"However,":[111],"them":[114],"neglect":[115],"correlations":[116],"among":[117],"combinations,":[119],"tailored":[122],"specific":[124],"applications,":[125],"resulting":[126],"poor":[128],"adaptability":[129],"different":[131,224],"scenarios.Besides,":[133],"sophisticated":[134],"cause":[139],"high":[140],"space":[143],"complexity.":[144],"still":[147],"lack":[149],"efficient":[152],"highly":[153],"scalable":[154],"adaptable":[156],"solution":[157],"accurate":[159],"To":[164],"address":[165],"this":[166,169],"issue,":[167],"work,":[170],"we":[171],"conduct":[172],"thorough":[174],"analysis":[175],"data":[180,252],"propose":[182],"novel":[184],"spatial-temporal":[186],"tensor":[187],"framework,":[189],"provides":[191],"general":[193],"design":[194],"We":[200],"deployed":[201],"framework":[204,221,233],"Tencent":[206],"guaranteed":[208],"delivery":[209],"system,":[211],"extensively":[213],"evaluated":[214],"effectiveness":[216],"efficiency":[218],"two":[223],"application":[226],"scenarios.":[227],"results":[229],"show":[230],"that":[231],"our":[232],"outperforms":[234],"existing":[235],"methods":[236],"prediction":[238],"accuracy.":[239],"Meanwhile,":[240],"it":[241],"significantly":[242],"reduces":[243],"parameter":[245],"resistant":[249],"incomplete":[251],"up":[254],"20%":[256],"missing":[257],"values.":[258]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
