{"id":"https://openalex.org/W4318185511","doi":"https://doi.org/10.1109/bigdata55660.2022.10020741","title":"Multi-criteria Rating and Review based Recommendation Model","display_name":"Multi-criteria Rating and Review based Recommendation Model","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318185511","doi":"https://doi.org/10.1109/bigdata55660.2022.10020741"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020741","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big 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/A5062870996","display_name":"Emrul Hasan","orcid":"https://orcid.org/0009-0009-3275-3158"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Emrul Hasan","raw_affiliation_strings":["Toronto Metropolitan University,Department of Computer Science,Toronto,Canada","Department of Computer Science, Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University,Department of Computer Science,Toronto,Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Computer Science, Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011372918","display_name":"Chen Ding","orcid":"https://orcid.org/0000-0001-8101-5738"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chen Ding","raw_affiliation_strings":["Toronto Metropolitan University,Department of Computer Science,Toronto,Canada","Department of Computer Science, Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University,Department of Computer Science,Toronto,Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Computer Science, Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053225136","display_name":"Alfredo Cuzzocrea","orcid":"https://orcid.org/0000-0002-7104-6415"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alfredo Cuzzocrea","raw_affiliation_strings":["University of Calabria,iDEA Lab,Rende,Italy","iDEA Lab, University of Calabria, Rende, Italy"],"affiliations":[{"raw_affiliation_string":"University of Calabria,iDEA Lab,Rende,Italy","institution_ids":["https://openalex.org/I45204951"]},{"raw_affiliation_string":"iDEA Lab, University of Calabria, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062870996"],"corresponding_institution_ids":["https://openalex.org/I530967"],"apc_list":null,"apc_paid":null,"fwci":0.437,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64109629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"5494","last_page":"5503"},"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":0.996999979019165,"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":0.9876999855041504,"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":0.7793254852294922},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7454074025154114},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.609795331954956},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6005061864852905},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5696532130241394},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5101943016052246},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46516942977905273},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.45615923404693604},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.45409271121025085},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.445247083902359},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.43339964747428894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4153464436531067},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.3823506534099579},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3607036769390106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08518558740615845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7793254852294922},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7454074025154114},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.609795331954956},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6005061864852905},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5696532130241394},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5101943016052246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46516942977905273},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.45615923404693604},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.45409271121025085},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.445247083902359},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.43339964747428894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4153464436531067},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3823506534099579},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3607036769390106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08518558740615845},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020741","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334971","display_name":"Science and Engineering Research Council","ror":"https://ror.org/00zgdb249"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W853600594","https://openalex.org/W1507547546","https://openalex.org/W1665214252","https://openalex.org/W1995797952","https://openalex.org/W2028988057","https://openalex.org/W2035256937","https://openalex.org/W2037351199","https://openalex.org/W2137245235","https://openalex.org/W2143017621","https://openalex.org/W2149846061","https://openalex.org/W2152184085","https://openalex.org/W2152561460","https://openalex.org/W2159457224","https://openalex.org/W2250539671","https://openalex.org/W2295739661","https://openalex.org/W2340502990","https://openalex.org/W2514530580","https://openalex.org/W2575006718","https://openalex.org/W2605350416","https://openalex.org/W2749348810","https://openalex.org/W2782139629","https://openalex.org/W2788376297","https://openalex.org/W2788893025","https://openalex.org/W2800447881","https://openalex.org/W2897660518","https://openalex.org/W2899626049","https://openalex.org/W2900806287","https://openalex.org/W2907069037","https://openalex.org/W2908054697","https://openalex.org/W2922306896","https://openalex.org/W2947936100","https://openalex.org/W2949655105","https://openalex.org/W2953343412","https://openalex.org/W2954351090","https://openalex.org/W2966937748","https://openalex.org/W2973142485","https://openalex.org/W2988857752","https://openalex.org/W2991276554","https://openalex.org/W3007223578","https://openalex.org/W3012192707","https://openalex.org/W3013050088","https://openalex.org/W3038904238","https://openalex.org/W3047560850","https://openalex.org/W3097819776","https://openalex.org/W3102331315","https://openalex.org/W3116844197","https://openalex.org/W3137391436","https://openalex.org/W3138194008","https://openalex.org/W3165018541","https://openalex.org/W3204071868","https://openalex.org/W4206588201","https://openalex.org/W4225656840","https://openalex.org/W4237880677","https://openalex.org/W4239025696","https://openalex.org/W4281817583","https://openalex.org/W4294170691","https://openalex.org/W6637242042","https://openalex.org/W6638667902","https://openalex.org/W6680451568"],"related_works":["https://openalex.org/W4313384562","https://openalex.org/W4313247739","https://openalex.org/W2891550009","https://openalex.org/W3133560342","https://openalex.org/W2364562957","https://openalex.org/W4254304201","https://openalex.org/W2963874530","https://openalex.org/W2596026555","https://openalex.org/W2738734060","https://openalex.org/W4318185511"],"abstract_inverted_index":{"These":[0],"days,":[1],"due":[2],"to":[3,25,39,42],"the":[4,15,70,108,210],"advancement":[5],"of":[6,14,159,175],"information":[7],"technology,":[8],"recommendation":[9,62,71],"system":[10],"has":[11,135],"become":[12],"one":[13],"key":[16],"tools":[17],"for":[18],"e-commerce":[19],"business.":[20],"E-commerce":[21],"platforms":[22],"allow":[23],"users":[24,41],"provide":[26],"feedback":[27],"in":[28,157],"both":[29,95],"written":[30],"comments":[31,52],"and":[32,101,118,152,164,177,181,184,187,192],"numerical":[33,102],"ratings.":[34,54],"Recommendation":[35,121,155],"systems":[36,63],"are":[37,78],"utilized":[38],"recommend":[40],"new":[43],"or":[44,53,59,83],"unseen":[45],"items":[46],"based":[47,61,120],"on":[48,139],"these":[49,75],"previously":[50],"collected":[51],"In":[55,88],"recent":[56],"years,":[57],"multi-aspect":[58,131],"multi-criteria":[60,103,128],"have":[64],"been":[65],"studied":[66],"a":[67,115,136],"lot":[68],"by":[69],"research":[72,76],"community.":[73],"However,":[74],"works":[77],"conducted":[79],"either":[80],"with":[81,86,205],"reviews":[82,97,134],"ratings,":[84],"not":[85],"both.":[87],"this":[89],"project,":[90],"we":[91],"argue":[92],"that":[93,126,168,199],"integrating":[94],"textual":[96],"(with":[98],"multiple":[99],"aspects)":[100],"ratings":[104,129,132],"can":[105],"further":[106,197],"enhance":[107],"overall":[109],"rating":[110],"prediction":[111],"accuracy.":[112],"We":[113,124,166,196],"propose":[114],"Multi-criteria":[116,154],"Rating":[117],"Review":[119],"model":[122,143,171,201],"(MRRRec).":[123],"show":[125,167,198],"incorporating":[127],"into":[130],"from":[133],"great":[137],"impact":[138],"performance.":[140],"Our":[141],"proposed":[142,170],"outperforms":[144],"several":[145],"state-of-the-art":[146],"models":[147],"such":[148],"as":[149],"ANR,":[150],"DeepCoNN,":[151],"Deep":[153],"System":[156],"terms":[158],"MSE,":[160],"MAE,":[161],"precision,":[162,190],"recall,":[163,191],"F1.":[165],"our":[169,200],"achieves":[172],"an":[173],"average":[174],"19%":[176],"23.0%":[178],"lower":[179],"MSE":[180],"MAE":[182],"respectively":[183],"7.0%,":[185],"1.0%":[186],"3.8%":[188],"higher":[189],"F1":[193],"score":[194],"respectively.":[195],"performs":[202],"significantly":[203],"better":[204],"Word2Vec":[206],"word":[207,212],"embedding":[208,213],"than":[209],"GloVe":[211],"method.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
