{"id":"https://openalex.org/W2298076993","doi":"https://doi.org/10.1108/k-10-2014-0212","title":"A collaborative filtering similarity measure based on potential field","display_name":"A collaborative filtering similarity measure based on potential field","publication_year":2016,"publication_date":"2016-02-29","ids":{"openalex":"https://openalex.org/W2298076993","doi":"https://doi.org/10.1108/k-10-2014-0212","mag":"2298076993"},"language":"en","primary_location":{"id":"doi:10.1108/k-10-2014-0212","doi":"https://doi.org/10.1108/k-10-2014-0212","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-10-2014-0212","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-40","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5100349248","display_name":"Ya-Jun Leng","orcid":"https://orcid.org/0000-0001-9168-5866","observed_orcids":["https://orcid.org/0000-0001-9168-5866"]},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajun Leng","raw_affiliation_strings":["College of Economics and Management, Shanghai University of Electric Power, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics and Management, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011459062","display_name":"Qing Lu","orcid":"https://orcid.org/0000-0002-4847-207X","observed_orcids":["https://orcid.org/0000-0002-4847-207X"]},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Lu","raw_affiliation_strings":["College of Economics and Management, Shanghai University of Electric Power, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics and Management, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102023161","display_name":"Changyong Liang","orcid":"https://orcid.org/0000-0001-9156-3885","observed_orcids":["https://orcid.org/0000-0001-9156-3885"]},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changyong Liang","raw_affiliation_strings":["School of Management, Hefei University of Technology, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management, Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2019,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93615575,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":98},"biblio":{"volume":"45","issue":"3","first_page":"434","last_page":"445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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/T14435","display_name":"Information Retrieval and Data Mining","score":0.0,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"collaborative filtering","score":0.9950000047683716},{"id":"https://openalex.org/keywords/similarity-measures","display_name":"similarity measures","score":0.7789999842643738},{"id":"https://openalex.org/keywords/potential-field","display_name":"potential field","score":0.972000002861023},{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.8980000019073486},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.9440000057220459},{"id":"https://openalex.org/keywords/common-neighbors","display_name":"common neighbors","score":0.7699999809265137},{"id":"https://openalex.org/keywords/rating-matrix","display_name":"rating matrix","score":0.3970000147819519},{"id":"https://openalex.org/keywords/personalized-recommendation","display_name":"personalized recommendation","score":0.4830000102519989}],"concepts":[{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.8772420883178711},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7954937219619751},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7762120366096497},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711894690990448},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5609012842178345},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5508471131324768},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5466597080230713},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5323607921600342},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5300588011741638},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5283651351928711},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39654457569122314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3466300964355469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21120288968086243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.20171934366226196},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/k-10-2014-0212","doi":"https://doi.org/10.1108/k-10-2014-0212","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-10-2014-0212","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-40","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309636","display_name":"University of Minnesota","ror":"https://ror.org/03grvy078"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1559689999","https://openalex.org/W1570159982","https://openalex.org/W1589148151","https://openalex.org/W1832221731","https://openalex.org/W1965443316","https://openalex.org/W1967229875","https://openalex.org/W1974848861","https://openalex.org/W1980075028","https://openalex.org/W1997874979","https://openalex.org/W2007508006","https://openalex.org/W2007595256","https://openalex.org/W2018597607","https://openalex.org/W2026962484","https://openalex.org/W2036369530","https://openalex.org/W2041206926","https://openalex.org/W2050248182","https://openalex.org/W2050802111","https://openalex.org/W2052091171","https://openalex.org/W2066330346","https://openalex.org/W2072955302","https://openalex.org/W2079459186","https://openalex.org/W2111778013","https://openalex.org/W2117311203","https://openalex.org/W2118950593","https://openalex.org/W2122788739","https://openalex.org/W2133446576","https://openalex.org/W2155106456","https://openalex.org/W2171960770","https://openalex.org/W2359344853"],"related_works":["https://openalex.org/W1965757506","https://openalex.org/W4366540497","https://openalex.org/W2085082165","https://openalex.org/W4387975413","https://openalex.org/W2193373641","https://openalex.org/W2806888275","https://openalex.org/W3039152657","https://openalex.org/W4252447396","https://openalex.org/W2612144299","https://openalex.org/W2899459113"],"abstract_inverted_index":{"Purpose":[0],"\u2013":[1,101,190,206],"Collaborative":[2],"recommender":[3,28,41,283],"systems":[4,284],"play":[5],"a":[6,36,92,106,219],"crucial":[7],"role":[8,38],"in":[9,39,81,282,292],"providing":[10],"personalized":[11],"services":[12],"to":[13,62,90,116,149,200,229,278,289],"online":[14,17],"consumers.":[15],"Most":[16],"shopping":[18],"sites":[19],"and":[20,56,112,225,260],"many":[21],"other":[22],"applications":[23],"now":[24],"use":[25,226],"the":[26,33,45,67,74,123,135,143,150,153,160,172,184,195,201,216,235,247,255,261,267,279],"collaborative":[27,40],"systems.":[29,42],"The":[30,84,102,167,207,271],"measurement":[31],"of":[32,44,66,76,86,174,177,186,210,275,294],"similarity":[34,48,55,69,94,138,168,187,203,250,269],"plays":[35],"fundamental":[37],"Some":[43],"most":[46],"well-known":[47],"measures":[49,70],"are:":[50],"Pearson\u2019s":[51],"correlation":[52],"coefficient,":[53],"cosine":[54],"mean":[57],"squared":[58],"differences.":[59],"However,":[60],"due":[61],"data":[63],"sparsity,":[64],"accuracy":[65,185],"above":[68],"decreases,":[71],"which":[72,180],"makes":[73],"formation":[75],"inaccurate":[77],"neighborhood,":[78],"thereby":[79],"resulting":[80],"poor":[82],"recommendations.":[83],"purpose":[85],"this":[87,114,211,227],"paper":[88,212],"is":[89,156,198],"propose":[91],"novel":[93],"measure":[95],"based":[96,128,240],"on":[97,129,241],"potential":[98,110,118,124,154,223,231,236],"field.":[99],"Design/methodology/approach":[100],"proposed":[103,196,262],"approach":[104,197,263],"constructs":[105],"dense":[107,220],"matrix:":[108,221],"user-user":[109,222],"matrix,":[111,152,224],"uses":[113],"matrix":[115,155,228],"compute":[117,230],"similarities":[119,125,232,237],"between":[120,233],"users.":[121],"Then":[122],"are":[126,140,213,238,252],"modified":[127,137,239,249],"users\u2019":[130,242],"preliminary":[131,243],"neighborhoods,":[132,244],"andkusers":[133,245],"with":[134,246],"highest":[136,248],"values":[139,251],"selected":[141,253],"as":[142,214,254,286,288],"active":[144,256],"user\u2019s":[145,257],"nearest":[146,258],"neighbors.":[147],"Compared":[148],"rating":[151],"much":[157],"denser.":[158],"Thus,":[159],"sparsity":[161],"problem":[162],"can":[163,181],"be":[164,274],"efficiently":[165],"alleviated.":[166],"modification":[169],"scheme":[170],"considers":[171],"number":[173],"common":[175],"neighbors":[176],"two":[178],"users,":[179],"further":[182],"improve":[183],"computation.":[188],"Findings":[189],"Experimental":[191],"results":[192],"show":[193],"that":[194],"superior":[199],"traditional":[202,268],"measures.":[204,270],"Originality/value":[205],"research":[208,285],"highlights":[209],"follows:":[215],"authors":[217],"construct":[218],"users;":[234],"neighbors;":[259],"performs":[264],"better":[265],"than":[266],"manuscript":[272],"will":[273],"particular":[276],"interests":[277],"scientists":[280],"interested":[281,291],"well":[287],"readers":[290],"solution":[293],"related":[295],"complex":[296],"practical":[297],"engineering":[298],"problems.":[299]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2016-06-24T00:00:00"}