{"id":"https://openalex.org/W2605148104","doi":"https://doi.org/10.1145/3079628.3079685","title":"Weighted Random Walk Sampling for Multi-Relational Recommendation","display_name":"Weighted Random Walk Sampling for Multi-Relational Recommendation","publication_year":2017,"publication_date":"2017-07-07","ids":{"openalex":"https://openalex.org/W2605148104","doi":"https://doi.org/10.1145/3079628.3079685","mag":"2605148104"},"language":"en","primary_location":{"id":"doi:10.1145/3079628.3079685","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3079628.3079685","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","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/A5065585826","display_name":"Fatemeh Vahedian","orcid":null},"institutions":[{"id":"https://openalex.org/I118353179","display_name":"DePaul University","ror":"https://ror.org/04xtx5t16","country_code":"US","type":"education","lineage":["https://openalex.org/I118353179"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fatemeh Vahedian","raw_affiliation_strings":["DePaul University, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"DePaul University, Chicago, IL, USA","institution_ids":["https://openalex.org/I118353179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043134791","display_name":"Robin Burke","orcid":"https://orcid.org/0000-0001-5766-6434"},"institutions":[{"id":"https://openalex.org/I118353179","display_name":"DePaul University","ror":"https://ror.org/04xtx5t16","country_code":"US","type":"education","lineage":["https://openalex.org/I118353179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robin Burke","raw_affiliation_strings":["DePaul University, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"DePaul University, Chicago, IL, USA","institution_ids":["https://openalex.org/I118353179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082580430","display_name":"Bamshad Mobasher","orcid":"https://orcid.org/0000-0001-9701-9178"},"institutions":[{"id":"https://openalex.org/I118353179","display_name":"DePaul University","ror":"https://ror.org/04xtx5t16","country_code":"US","type":"education","lineage":["https://openalex.org/I118353179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bamshad Mobasher","raw_affiliation_strings":["DePaul University, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"DePaul University, Chicago, IL, USA","institution_ids":["https://openalex.org/I118353179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065585826"],"corresponding_institution_ids":["https://openalex.org/I118353179"],"apc_list":null,"apc_paid":null,"fwci":2.9018,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.92328652,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"230","last_page":"237"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9861999750137329,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9409000277519226,"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/recommender-system","display_name":"Recommender system","score":0.8425844311714172},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8284697532653809},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5908970236778259},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5394402742385864},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5388969779014587},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.530996561050415},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.52097088098526},{"id":"https://openalex.org/keywords/binary-relation","display_name":"Binary relation","score":0.41089293360710144},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4099433422088623},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36156463623046875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33918797969818115},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32250723242759705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10537132620811462},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07635834813117981}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8425844311714172},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8284697532653809},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5908970236778259},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5394402742385864},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5388969779014587},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.530996561050415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.52097088098526},{"id":"https://openalex.org/C65180967","wikidata":"https://www.wikidata.org/wiki/Q130901","display_name":"Binary relation","level":2,"score":0.41089293360710144},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4099433422088623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36156463623046875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33918797969818115},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32250723242759705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10537132620811462},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07635834813117981},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3079628.3079685","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3079628.3079685","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2688606604","display_name":null,"funder_award_id":"IIS-1423368","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1632866817","https://openalex.org/W2010187764","https://openalex.org/W2036140203","https://openalex.org/W2042900885","https://openalex.org/W2044051625","https://openalex.org/W2047729491","https://openalex.org/W2061757435","https://openalex.org/W2070700141","https://openalex.org/W2111094216","https://openalex.org/W2117420919","https://openalex.org/W2138759931","https://openalex.org/W2171357718","https://openalex.org/W2188704965","https://openalex.org/W2294384044","https://openalex.org/W2397423854","https://openalex.org/W2400838948","https://openalex.org/W2563934921","https://openalex.org/W2576312337"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W3092950680","https://openalex.org/W4246980185","https://openalex.org/W2150182025","https://openalex.org/W3197542405","https://openalex.org/W2418190244","https://openalex.org/W4238861846","https://openalex.org/W3125580266","https://openalex.org/W44246808"],"abstract_inverted_index":{"In":[0,140],"the":[1,85,89,152],"information":[2,78,120],"overloaded":[3],"web,":[4],"personalized":[5],"recommender":[6,67],"systems":[7],"are":[8,27,135],"essential":[9],"tools":[10],"to":[11,75,107],"help":[12],"users":[13,81],"find":[14],"most":[15,19],"relevant":[16],"information.":[17],"The":[18],"heavily-used":[20],"recommendation":[21,39,189],"frameworks":[22],"assume":[23],"user":[24,127],"interactions":[25,44],"that":[26,71],"characterized":[28],"by":[29],"a":[30,49,57,145,158],"single":[31,58],"relation.":[32,59],"However,":[33,119],"for":[34,133],"many":[35],"tasks,":[36],"such":[37,94,125,138],"as":[38,48,126],"in":[40,84,93,137,150,169,186],"social":[41],"networks,":[42,105],"user-item":[43],"must":[45,113],"be":[46,114,131],"modeled":[47],"complex":[50],"network":[51,86],"of":[52,91,97,154,160,188],"multiple":[53,182],"relations,":[54],"not":[55],"only":[56],"Recently":[60],"research":[61],"on":[62,102,181],"multi-relational":[63],"factorization":[64],"and":[65,82,106,163,191],"hybrid":[66],"models":[68],"has":[69],"shown":[70],"using":[72],"extended":[73,167],"meta-paths":[74,168],"capture":[76],"additional":[77],"about":[79],"both":[80,185],"items":[83],"can":[87],"enhance":[88],"accuracy":[90,190],"recommendations":[92],"networks.":[95,172],"Most":[96],"this":[98,141,165,174],"work":[99],"is":[100,157],"focused":[101],"unweighted":[103],"heterogeneous":[104,171],"apply":[108,164],"these":[109],"techniques,":[110],"weighted":[111,123,170],"relations":[112],"simplified":[115],"into":[116],"binary":[117],"ones.":[118],"associated":[121],"with":[122],"edges,":[124],"ratings,":[128],"which":[129,151],"may":[130],"crucial":[132],"recommendation,":[134],"lost":[136],"binarization.":[139],"paper,":[142],"we":[143,177],"explore":[144],"random":[146],"walk":[147],"sampling":[148,156,175],"method":[149],"frequency":[153],"edge":[155,161],"function":[159],"weight,":[162],"generate":[166],"With":[173],"technique,":[176],"demonstrate":[178],"improved":[179],"performance":[180],"data":[183],"sets":[184],"terms":[187],"model":[192],"generation":[193],"efficiency.":[194]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
