{"id":"https://openalex.org/W2790970052","doi":"https://doi.org/10.1145/3152494.3152505","title":"A neural attention based approach for clickstream mining","display_name":"A neural attention based approach for clickstream mining","publication_year":2018,"publication_date":"2018-01-11","ids":{"openalex":"https://openalex.org/W2790970052","doi":"https://doi.org/10.1145/3152494.3152505","mag":"2790970052"},"language":"en","primary_location":{"id":"doi:10.1145/3152494.3152505","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3152494.3152505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM India Joint International Conference on Data Science and Management of 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/A5088702417","display_name":"T. Chandramohan","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"facility","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Chandramohan T N","raw_affiliation_strings":["IIT Madras, Chennai, India"],"affiliations":[{"raw_affiliation_string":"IIT Madras, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009374923","display_name":"Balaraman Ravindran","orcid":"https://orcid.org/0000-0002-5364-7639"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"facility","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Balaraman Ravindran","raw_affiliation_strings":["IIT Madras, Chennai, India"],"affiliations":[{"raw_affiliation_string":"IIT Madras, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088702417"],"corresponding_institution_ids":["https://openalex.org/I24676775"],"apc_list":null,"apc_paid":null,"fwci":2.3561,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.91080519,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9937999844551086,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9937999844551086,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13083","display_name":"Advanced Text Analysis 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/clickstream","display_name":"Clickstream","score":0.9237633943557739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8105124235153198},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6478602290153503},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6292937397956848},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6235409379005432},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5674359798431396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49269014596939087},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.46014532446861267},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.41391873359680176},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.17640623450279236},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.17151916027069092}],"concepts":[{"id":"https://openalex.org/C138744977","wikidata":"https://www.wikidata.org/wiki/Q5132438","display_name":"Clickstream","level":5,"score":0.9237633943557739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8105124235153198},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6478602290153503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6292937397956848},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6235409379005432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5674359798431396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49269014596939087},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.46014532446861267},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.41391873359680176},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.17640623450279236},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.17151916027069092},{"id":"https://openalex.org/C127613066","wikidata":"https://www.wikidata.org/wiki/Q557770","display_name":"Web API","level":4,"score":0.0},{"id":"https://openalex.org/C130436687","wikidata":"https://www.wikidata.org/wiki/Q7978591","display_name":"Web modeling","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3152494.3152505","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3152494.3152505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM India Joint International Conference on Data Science and Management of Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W1514091028","https://openalex.org/W1570642533","https://openalex.org/W1614298861","https://openalex.org/W1964378344","https://openalex.org/W1994099982","https://openalex.org/W2017121215","https://openalex.org/W2026091747","https://openalex.org/W2035342005","https://openalex.org/W2053915893","https://openalex.org/W2056590539","https://openalex.org/W2057898995","https://openalex.org/W2064675550","https://openalex.org/W2100495367","https://openalex.org/W2102443632","https://openalex.org/W2112971308","https://openalex.org/W2113442785","https://openalex.org/W2125441033","https://openalex.org/W2143503258","https://openalex.org/W2151987718","https://openalex.org/W2153635508","https://openalex.org/W2195229545","https://openalex.org/W2296366548","https://openalex.org/W2326760547","https://openalex.org/W2338406834","https://openalex.org/W3125952450","https://openalex.org/W3152364157","https://openalex.org/W4230906481"],"related_works":["https://openalex.org/W1493153155","https://openalex.org/W2789872759","https://openalex.org/W2154850984","https://openalex.org/W2975622968","https://openalex.org/W2395287825","https://openalex.org/W2999859704","https://openalex.org/W4389608763","https://openalex.org/W2352626522","https://openalex.org/W2969315860","https://openalex.org/W2149098859"],"abstract_inverted_index":{"E-commerce":[0],"has":[1],"seen":[2],"tremendous":[3],"growth":[4],"over":[5,306],"the":[6,27,100,116,185,212,216,228,246,252,279],"past":[7],"few":[8],"years,":[9],"so":[10,12],"much":[11,290],"that":[13,151,176,278,296,315],"only":[14,255],"those":[15],"companies":[16],"which":[17,42,109,225],"analyze":[18],"browsing":[19,68],"behaviour":[20,34,69],"of":[21,54,63,70,139,215,227,259,304],"users,":[22],"can":[23],"hope":[24],"to":[25,45,66,76,111,172,179,219,222,224,262,285],"survive":[26],"stiff":[28],"competition":[29],"in":[30,36,88,167,251,289],"market.":[31],"Analyzing":[32],"customer":[33],"helps":[35],"modeling":[37],"and":[38,51,79,85,119,287],"recognizing":[39],"purchase":[40,158],"intent":[41],"is":[43,240,245,301],"vital":[44],"e-commerce":[46],"for":[47,91],"providing":[48],"improved":[49,291],"personalization":[50],"better":[52,321],"ranking":[53],"search":[55],"results.":[56],"In":[57,141],"this":[58,142],"work,":[59,143],"we":[60,123,144],"make":[61,156],"use":[62],"user":[64],"clickstreams":[65,73,84,108,125,260],"model":[67,95,124,280],"users.":[71],"But":[72],"are":[74,104,110,177,196,203],"known":[75,193],"be":[77,112],"noisy":[78],"hence":[80],"generating":[81],"features":[82,183],"from":[83,184],"using":[86,206,271,297,317],"them":[87],"one":[89],"go":[90],"building":[92],"a":[93,137,146,157,256,266],"predictive":[94],"may":[96],"not":[97,160,283],"always":[98],"capture":[99],"purchase/intent":[101],"characteristics.":[102],"There":[103],"multiple":[105,128],"aspects":[106],"within":[107],"considered":[113],"such":[114,326],"as":[115,126,194,327],"sequence":[117],"(path)":[118],"temporal":[120],"behaviour.":[121],"Hence":[122],"having":[127],"views,":[129],"each":[130,199],"view,":[131],"concentrating":[132],"on":[133,198,236],"an":[134,207,302],"aspect":[135],"or":[136,159],"component":[138],"clickstream.":[140],"develop":[145],"Multi-View":[147],"learning":[148,169],"(MVL)":[149],"framework":[150],"predicts":[152],"whether":[153],"users":[154],"would":[155],"by":[161],"analyzing":[162],"their":[163],"clickstreams.":[164],"Recent":[165],"advances":[166],"deep":[168],"allow":[170],"us":[171],"build":[173],"neural":[174],"networks":[175],"able":[178],"extract":[180],"complex":[181],"latent":[182],"data":[186,253],"with":[187],"minimal":[188],"human":[189],"intervention.":[190],"Separate":[191],"models":[192],"experts":[195,202],"trained":[197,281],"view.":[200],"The":[201],"then":[204],"combined":[205],"Expert-Attention":[208],"(EA)":[209],"network,":[210],"where":[211],"attention":[213],"part":[214],"network":[217,239,319],"tries":[218],"learn":[220],"when":[221],"attend":[223],"view":[226,308],"data.":[229],"Multiple":[230,328],"variants":[231],"have":[232],"been":[233],"proposed":[234],"based":[235],"how":[237],"EA":[238,298,318],"trained.":[241],"Yet":[242],"another":[243],"challenge":[244],"extreme":[247],"class":[248],"imbalance":[249],"present":[250],"since":[254],"small":[257],"fraction":[258],"correspond":[261],"buyers.":[263],"We":[264],"propose":[265],"well":[267],"informed":[268],"undersampling":[269,275],"strategy":[270],"autoencoders.":[272],"This":[273],"simple":[274],"technique":[276],"ensured":[277],"was":[282,312],"biased":[284],"non-buyers":[286],"resulted":[288],"f-scores.":[292],"Experimental":[293],"results":[294],"show":[295],"networks,":[299],"there":[300],"improvement":[303],"13%":[305],"single":[307],"methods.":[309],"Moreover,":[310],"it":[311],"also":[313],"noticed":[314],"MVL":[316,324],"performs":[320],"than":[322],"conventional":[323],"methods":[325],"Kernel":[329],"Learning.":[330]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}