{"id":"https://openalex.org/W2742205322","doi":"https://doi.org/10.1145/3106426.3106439","title":"Enhancing long tail item recommendations using tripartite graphs and Markov process","display_name":"Enhancing long tail item recommendations using tripartite graphs and Markov process","publication_year":2017,"publication_date":"2017-08-10","ids":{"openalex":"https://openalex.org/W2742205322","doi":"https://doi.org/10.1145/3106426.3106439","mag":"2742205322"},"language":"en","primary_location":{"id":"doi:10.1145/3106426.3106439","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3106439","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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":"Proceedings of the International Conference on Web Intelligence","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/A5013670342","display_name":"Joseph Johnson","orcid":"https://orcid.org/0000-0001-9576-1045"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Johnson","raw_affiliation_strings":["Brigham Young University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young University","institution_ids":["https://openalex.org/I100005738"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086456104","display_name":"Yiu\u2010Kai Ng","orcid":"https://orcid.org/0000-0002-5680-2796"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiu-Kai Ng","raw_affiliation_strings":["Brigham Young University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young University","institution_ids":["https://openalex.org/I100005738"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.888,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.93090393,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"761","last_page":"768"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7437302470207214},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7094085216522217},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6750374436378479},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4611530303955078},{"id":"https://openalex.org/keywords/movielens","display_name":"MovieLens","score":0.42531198263168335},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.41524824500083923},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4134112596511841},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3465290069580078},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.3348481059074402},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3259107768535614},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31622761487960815},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1421392858028412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7437302470207214},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7094085216522217},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6750374436378479},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4611530303955078},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.42531198263168335},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.41524824500083923},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4134112596511841},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3465290069580078},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.3348481059074402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3259107768535614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31622761487960815},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1421392858028412},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3106426.3106439","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3106439","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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":"Proceedings of the International Conference on Web Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1488971269","https://openalex.org/W1532325895","https://openalex.org/W1690919088","https://openalex.org/W1972156947","https://openalex.org/W2023954349","https://openalex.org/W2042281163","https://openalex.org/W2051082454","https://openalex.org/W2054141820","https://openalex.org/W2066827437","https://openalex.org/W2105953200","https://openalex.org/W2124756613","https://openalex.org/W2132314908","https://openalex.org/W2138899136","https://openalex.org/W2140942692","https://openalex.org/W2148117599","https://openalex.org/W2153190022","https://openalex.org/W2162868779","https://openalex.org/W2171960770","https://openalex.org/W2474047286","https://openalex.org/W2520082712","https://openalex.org/W2963993568"],"related_works":["https://openalex.org/W2797500822","https://openalex.org/W2355698112","https://openalex.org/W2022984797","https://openalex.org/W2986679525","https://openalex.org/W4205822456","https://openalex.org/W4299358966","https://openalex.org/W2537367858","https://openalex.org/W2981634480","https://openalex.org/W4288082747","https://openalex.org/W2188396403"],"abstract_inverted_index":{"Given":[0],"that":[1,115,133,173,266],"the":[2,20,30,48,57,73,85,96,104,116,124,157,160,189,211,243,251,263],"Internet":[3],"and":[4,16,152,224],"sophisticated":[5],"transportation":[6],"networks":[7],"have":[8],"made":[9],"an":[10,112],"increasingly":[11],"huge":[12],"number":[13],"of":[14,44,53,76,88,177,182,201,247,262],"products":[15,43,78,90,143],"services":[17],"available":[18,79],"to":[19,25,36,47,56,179,220,241,269],"public,":[21],"consumers":[22,83],"are":[23,267],"unable":[24],"identify,":[26],"much":[27],"less":[28],"evaluate":[29],"usefulness":[31],"of,":[32],"such":[33],"goods":[34],"accessible":[35],"them.":[37],"Modern":[38],"recommendation":[39,62],"systems":[40,63,136],"filter":[41],"out":[42],"lesser":[45],"utility":[46],"customer,":[49],"showcasing":[50],"those":[51],"items":[52],"higher":[54],"preference":[55],"user.":[58],"While":[59,148],"current":[60],"state-of-the-art":[61],"perform":[64],"fairly":[65],"well,":[66],"they":[67],"generally":[68],"do":[69],"better":[70,164],"at":[71],"recommending":[72],"popular":[74],"subset":[75],"all":[77],"rather":[80],"than":[81,144],"matching":[82],"with":[84],"vast":[86],"amount":[87],"niche":[89],"in":[91,218],"what":[92],"has":[93],"been":[94],"termed":[95],"\"Long":[97],"Tail\".":[98],"In":[99,184],"their":[100,128],"seminal":[101],"work,":[102,232],"\"Challenging":[103],"Long":[105],"Tail":[106],"Recommendation\",":[107],"Yin":[108,193],"et":[109,194,207],"al.":[110],"make":[111],"eloquent":[113],"argument":[114],"long":[117,141,161,252,264],"tail":[118,142,162,253,265],"is":[119,163],"where":[120],"organizations":[121],"can":[122],"create":[123],"most":[125],"value":[126],"for":[127,140,145,156],"consumers.":[129],"They":[130],"also":[131],"argue":[132],"existing":[134],"recommender":[135],"operate":[137],"fundamentally":[138],"different":[139],"mainstream":[146],"goods.":[147,183],"matrix":[149],"factorization,":[150],"nearest-neighbors,":[151],"clustering":[153],"work":[154],"well":[155,197,234],"\"head\"":[158],"market,":[159],"represented":[165],"by":[166,192,205],"a":[167,170,175,180,199,215,238],"graph,":[168],"specifically":[169,257],"bipartite":[171,212],"graph":[172],"connects":[174],"set":[176,181,200],"users":[178,223],"this":[185],"paper,":[186],"we":[187],"discuss":[188],"algorithms":[190,203],"presented":[191],"al.,":[195,208],"as":[196,198,233,235],"similar":[202,222],"proposed":[204],"Shang":[206],"which":[209],"traverse":[210],"graphs":[213,249],"through":[214],"random":[216,244],"walker":[217],"order":[219],"identify":[221],"products.":[225],"We":[226],"build":[227],"on":[228],"elements":[229,236],"from":[230,237],"each":[231],"Markov":[239],"process,":[240],"facilitate":[242],"walker's":[245],"traversal":[246],"tripartitle":[248],"into":[250,260],"regions.":[254],"This":[255],"method":[256],"constructs":[258],"paths":[259],"regions":[261],"favorable":[268],"users.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
