{"id":"https://openalex.org/W2749348810","doi":"https://doi.org/10.1145/3109859.3109890","title":"Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction","display_name":"Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction","publication_year":2017,"publication_date":"2017-08-24","ids":{"openalex":"https://openalex.org/W2749348810","doi":"https://doi.org/10.1145/3109859.3109890","mag":"2749348810"},"language":"en","primary_location":{"id":"doi:10.1145/3109859.3109890","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109859.3109890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","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/A5069084905","display_name":"Sungyong Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sungyong Seo","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101625625","display_name":"Jing Huang","orcid":"https://orcid.org/0000-0003-1438-5071"},"institutions":[{"id":"https://openalex.org/I4210148469","display_name":"Visa (United States)","ror":"https://ror.org/05t1y0b59","country_code":"US","type":"company","lineage":["https://openalex.org/I4210148469"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Huang","raw_affiliation_strings":["Visa Research, Visa Inc., Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"Visa Research, Visa Inc., Palo Alto, CA, USA","institution_ids":["https://openalex.org/I4210148469"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090632160","display_name":"Hao Yang","orcid":"https://orcid.org/0000-0003-2214-9474"},"institutions":[{"id":"https://openalex.org/I4210148469","display_name":"Visa (United States)","ror":"https://ror.org/05t1y0b59","country_code":"US","type":"company","lineage":["https://openalex.org/I4210148469"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Yang","raw_affiliation_strings":["Visa Research, Visa Inc., Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"Visa Research, Visa Inc., Palo Alto, CA, USA","institution_ids":["https://openalex.org/I4210148469"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351175","display_name":"Yan Liu","orcid":"https://orcid.org/0000-0003-4242-4840"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Liu","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069084905"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":96.6274,"has_fulltext":false,"cited_by_count":461,"citation_normalized_percentile":{"value":0.99945326,"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":"297","last_page":"305"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9990000128746033,"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/T10028","display_name":"Topic Modeling","score":0.9977999925613403,"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":0.840401291847229},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6799342036247253},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6394282579421997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5463389754295349},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5101438164710999},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.42839592695236206},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.42628592252731323},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4255502223968506},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.41774022579193115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.840401291847229},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6799342036247253},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6394282579421997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5463389754295349},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5101438164710999},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.42839592695236206},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.42628592252731323},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4255502223968506},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41774022579193115},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","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:10.1145/3109859.3109890","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109859.3109890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2957356226","display_name":null,"funder_award_id":"IIS-1254206","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"},{"id":"https://openalex.org/F4320308737","display_name":"Facebook","ror":"https://ror.org/01zbnvs85"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W39762900","https://openalex.org/W1514535095","https://openalex.org/W1799366690","https://openalex.org/W1880262756","https://openalex.org/W1902237438","https://openalex.org/W1938755728","https://openalex.org/W1969245231","https://openalex.org/W1991418309","https://openalex.org/W2027731328","https://openalex.org/W2049434052","https://openalex.org/W2050096199","https://openalex.org/W2054141820","https://openalex.org/W2061873838","https://openalex.org/W2131876387","https://openalex.org/W2133564696","https://openalex.org/W2152184085","https://openalex.org/W2157881433","https://openalex.org/W2170240176","https://openalex.org/W2187089797","https://openalex.org/W2250539671","https://openalex.org/W2251292973","https://openalex.org/W2251939518","https://openalex.org/W2252215182","https://openalex.org/W2413904250","https://openalex.org/W2470673105","https://openalex.org/W2512971201","https://openalex.org/W2515144511","https://openalex.org/W2607662938","https://openalex.org/W2950133940","https://openalex.org/W2950178297","https://openalex.org/W2963083845","https://openalex.org/W3102701984","https://openalex.org/W4231510805","https://openalex.org/W4365799834"],"related_works":["https://openalex.org/W2012531322","https://openalex.org/W2402761219","https://openalex.org/W4321487865","https://openalex.org/W2785900585","https://openalex.org/W4313906399","https://openalex.org/W2353730437","https://openalex.org/W2490303674","https://openalex.org/W4239306820","https://openalex.org/W2609066826","https://openalex.org/W4391266461"],"abstract_inverted_index":{"Recently,":[0],"many":[1],"e-commerce":[2],"websites":[3],"have":[4],"encouraged":[5],"their":[6],"users":[7,126,179],"to":[8,36,48,72,110],"rate":[9],"shopping":[10],"items":[11,128],"and":[12,27,52,63,84,102,117,127,169,175,180,195,197,208,212,229,251],"write":[13],"review":[14,17,79,86,163,191],"texts.":[15],"This":[16],"information":[18],"has":[19],"been":[20],"very":[21],"useful":[22],"for":[23,88,249],"understanding":[24],"user":[25,50,83,101,116],"preferences":[26,51,145],"item":[28,53,118],"properties,":[29],"as":[30,32,133],"well":[31],"enhancing":[33],"the":[34,68,95,123,157,161,166,184,199,205,213,242,257],"capability":[35],"make":[37],"personalized":[38],"recommendations":[39],"of":[40,70,99,160,178,233,260],"these":[41,115,247],"websites.":[42],"In":[43,238],"this":[44],"paper,":[45],"we":[46,240],"propose":[47],"model":[49,92,226],"properties":[54],"using":[55,77],"convolutional":[56,216],"neural":[57],"networks":[58,119],"(CNNs)":[59],"with":[60,201,223],"dual":[61,224],"local":[62,137,168],"global":[64,151,170],"attention,":[65],"motivated":[66],"by":[67,187],"superiority":[69],"CNNs":[71,154,222],"extract":[73],"complex":[74],"features.":[75],"By":[76],"aggregated":[78,85],"texts":[80],"from":[81,246,264],"a":[82,130,143],"text":[87],"an":[89,147,173],"item,":[90],"our":[91,265],"can":[93],"learn":[94],"unique":[96],"features":[97,106],"(embedding)":[98],"each":[100,103],"item.":[104],"These":[105,253],"are":[107],"then":[108],"used":[109],"predict":[111],"ratings.":[112],"We":[113,182],"train":[114],"jointly":[120],"which":[121],"enable":[122,172],"interaction":[124],"between":[125],"in":[129,193,231],"similar":[131],"way":[132],"matrix":[134,202,217],"factorization.":[135],"The":[136,150],"attention":[138,152,225],"provides":[139],"us":[140],"insight":[141],"on":[142,156,189],"user's":[144],"or":[146],"item's":[148],"properties.":[149],"helps":[153],"focus":[155],"semantic":[158],"meaning":[159],"whole":[162],"text.":[164],"Thus,":[165],"combined":[167],"attentions":[171],"interpretable":[174],"better-learned":[176],"representation":[177],"items.":[181],"validate":[183],"proposed":[185,215,221],"models":[186,248],"testing":[188],"popular":[190],"datasets":[192],"Yelp":[194],"Amazon":[196],"compare":[198,241],"results":[200,254],"factorization":[203,218],"(MF),":[204],"hidden":[206],"factor":[207],"topical":[209],"(HFT)":[210],"model,":[211],"recently":[214],"(ConvMF+).":[219],"Our":[220],"outperforms":[227],"HFT":[228],"ConvMF+":[230],"terms":[232],"mean":[234],"square":[235],"errors":[236],"(MSE).":[237],"addition,":[239],"user/item":[243,261],"embeddings":[244,262],"learned":[245,263],"classification":[250],"recommendation.":[252],"also":[255],"confirm":[256],"superior":[258],"quality":[259],"model.":[266]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":43},{"year":2023,"cited_by_count":51},{"year":2022,"cited_by_count":56},{"year":2021,"cited_by_count":87},{"year":2020,"cited_by_count":83},{"year":2019,"cited_by_count":72},{"year":2018,"cited_by_count":34},{"year":2017,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}