{"id":"https://openalex.org/W2740992833","doi":"https://doi.org/10.1145/3077136.3080833","title":"User Interaction Sequences for Search Satisfaction Prediction","display_name":"User Interaction Sequences for Search Satisfaction Prediction","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2740992833","doi":"https://doi.org/10.1145/3077136.3080833","mag":"2740992833"},"language":"en","primary_location":{"id":"doi:10.1145/3077136.3080833","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3080833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5018503243","display_name":"Rishabh Mehrotra","orcid":"https://orcid.org/0000-0002-0836-4605"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Rishabh Mehrotra","raw_affiliation_strings":["University College London & Microsoft Research, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University College London & Microsoft Research, London, United Kingdom","institution_ids":["https://openalex.org/I4210164937","https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108365460","display_name":"Imed Zitouni","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Imed Zitouni","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021000040","display_name":"Ahmed Hassan Awadallah","orcid":"https://orcid.org/0000-0001-6426-3537"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Hassan Awadallah","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059208041","display_name":"Ahmed El Kholy","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed El Kholy","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054253075","display_name":"Madian Khabsa","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madian Khabsa","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018503243"],"corresponding_institution_ids":["https://openalex.org/I4210164937","https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":1.4564,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.89416955,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.992900013923645,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.992900013923645,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9916999936103821,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8155673742294312},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6405892968177795},{"id":"https://openalex.org/keywords/user-satisfaction","display_name":"User satisfaction","score":0.5982347130775452},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.44214388728141785},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4304409921169281},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.40976274013519287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36505138874053955},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34042608737945557},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33522462844848633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8155673742294312},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6405892968177795},{"id":"https://openalex.org/C3017893058","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"User satisfaction","level":2,"score":0.5982347130775452},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44214388728141785},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4304409921169281},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.40976274013519287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36505138874053955},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34042608737945557},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33522462844848633},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3077136.3080833","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3080833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W138372711","https://openalex.org/W157603173","https://openalex.org/W1590698834","https://openalex.org/W1929483471","https://openalex.org/W1973167214","https://openalex.org/W1973168503","https://openalex.org/W1973602439","https://openalex.org/W1975879668","https://openalex.org/W1976187638","https://openalex.org/W1982204215","https://openalex.org/W2003658560","https://openalex.org/W2007750197","https://openalex.org/W2012430612","https://openalex.org/W2034654712","https://openalex.org/W2035569891","https://openalex.org/W2038385982","https://openalex.org/W2060890758","https://openalex.org/W2064758233","https://openalex.org/W2069849731","https://openalex.org/W2096946253","https://openalex.org/W2106576032","https://openalex.org/W2106817091","https://openalex.org/W2117079628","https://openalex.org/W2120889650","https://openalex.org/W2123937625","https://openalex.org/W2134099522","https://openalex.org/W2137366811","https://openalex.org/W2147187250","https://openalex.org/W2150137742","https://openalex.org/W2153082595","https://openalex.org/W2154105380","https://openalex.org/W2158254843","https://openalex.org/W2158312787","https://openalex.org/W2158454296","https://openalex.org/W2164777277","https://openalex.org/W2166237624","https://openalex.org/W2167690205","https://openalex.org/W2170443290","https://openalex.org/W2341328702","https://openalex.org/W2341909854","https://openalex.org/W3150565720"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W1976205134","https://openalex.org/W2381570729","https://openalex.org/W4248336175","https://openalex.org/W3009369890","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W4312490297","https://openalex.org/W2062212388"],"abstract_inverted_index":{"Detecting":[0],"and":[1,31,45,73,101,132,181],"understanding":[2],"implicit":[3],"measures":[4],"of":[5,91,107,114,145,175,189,206],"user":[6,58,80,92,146,168,182,207],"satisfaction":[7,24],"are":[8,38],"essential":[9],"for":[10,41,78,203,210],"meaningful":[11],"experimentation":[12,150],"aimed":[13],"at":[14],"enhancing":[15],"web":[16],"search":[17,43,96],"quality.":[18],"While":[19],"most":[20],"existing":[21],"studies":[22],"on":[23,27,87],"prediction":[25],"rely":[26],"users'":[28],"click":[29,71],"activity":[30],"query":[32],"reformulation":[33],"behavior,":[34],"often":[35],"such":[36],"signals":[37,77],"not":[39,49],"available":[40],"all":[42],"sessions":[44],"as":[46,62,158],"a":[47,137,200],"result,":[48],"useful":[50,76],"in":[51,166,193],"predicting":[52,79,167],"satisfaction.":[53,81,169,183],"On":[54],"the":[55,95,116,155,176,187,190],"other":[56],"hand,":[57],"interaction":[59,93,105,118,208],"data":[60,72],"(such":[61],"mouse":[63],"cursor":[64],"movement)":[65],"is":[66],"far":[67],"richer":[68],"than":[69],"just":[70],"can":[74],"provide":[75,199],"In":[82,126],"this":[83],"work,":[84],"we":[85,135,151,185],"focus":[86],"considering":[88],"holistic":[89],"view":[90],"with":[94],"engine":[97],"result":[98],"page":[99],"(SERP)":[100],"construct":[102],"detailed":[103],"universal":[104,117],"sequences":[106,119],"their":[108],"activity.":[109],"We":[110,170],"propose":[111,136],"novel":[112],"ways":[113],"leveraging":[115],"to":[120,128,141,162],"automatically":[121],"extract":[122],"informative,":[123],"interpretable":[124],"subsequences.":[125],"addition":[127],"extracting":[129],"frequent,":[130],"discriminatory":[131],"interleaved":[133],"subsequences,":[134],"Hawkes":[138],"process":[139],"model":[140],"incorporate":[142],"temporal":[143],"aspects":[144],"interaction.":[147],"Through":[148],"extensive":[149],"show":[152],"that":[153],"encoding":[154],"extracted":[156],"subsequences":[157,180],"features":[159],"enables":[160],"us":[161],"achieve":[163],"significant":[164],"improvements":[165],"additionally":[171],"present":[172],"an":[173],"analysis":[174,205],"correlation":[177],"between":[178],"various":[179],"Finally,":[184],"demonstrate":[186],"usefulness":[188],"proposed":[191],"approach":[192],"covering":[194],"abandonment":[195],"cases.":[196],"Our":[197],"findings":[198],"valuable":[201],"tool":[202],"fine-grained":[204],"behavior":[209],"metric":[211],"development.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}