乐胖代购免代理版

{"id":"https://openalex.org/W2887332304","doi":"https://doi.org/10.1109/iri.2018.00015","title":"Movie Recommendations Using the Deep Learning Approach","display_name":"Movie Recommendations Using the Deep Learning Approach","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2887332304","doi":"https://doi.org/10.1109/iri.2018.00015","mag":"2887332304"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/iri.2018.00015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5082626147","display_name":"Jeffrey Lund","orcid":null},"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":"Jeffrey Lund","raw_affiliation_strings":["Computer Science Department, Brigham Young University, Provo, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Brigham Young University, Provo, USA","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":["Computer Science Department, Brigham Young University, Provo, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Brigham Young University, Provo, USA","institution_ids":["https://openalex.org/I100005738"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.464,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":42,"citation_normalized_percentile":{"value":0.832738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"47","last_page":"54"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997,"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":0.9997,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9467,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/movielens","display_name":"MovieLens","score":0.94842273},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5387922}],"concepts":[{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.94842273},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.90598786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8234661},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.80165},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.60705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6039485},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.57969713},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5387922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5132141},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43644753},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.43116593},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35996443},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.078467965},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/iri.2018.00015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":20,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W2013069124","https://openalex.org/W2017826930","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2114079787","https://openalex.org/W2117420919","https://openalex.org/W2141175775","https://openalex.org/W2143995004","https://openalex.org/W2148212498","https://openalex.org/W2157881433","https://openalex.org/W2194775991","https://openalex.org/W2219888463","https://openalex.org/W2314720829","https://openalex.org/W2533696134","https://openalex.org/W2565516711","https://openalex.org/W2739273093","https://openalex.org/W3122507327","https://openalex.org/W4231990774","https://openalex.org/W4251504464"],"related_works":["https://openalex.org/W4394818607","https://openalex.org/W4299358966","https://openalex.org/W4205822456","https://openalex.org/W3173811578","https://openalex.org/W2986679525","https://openalex.org/W2797500822","https://openalex.org/W2794458286","https://openalex.org/W2537367858","https://openalex.org/W2355698112","https://openalex.org/W2022984797"],"abstract_inverted_index":{"Recommendation":[0],"systems":[1,20],"are":[2,21],"an":[3],"important":[4],"part":[5],"of":[6,61,74,94,127],"suggesting":[7],"items":[8],"especially":[9],"in":[10,125,136,139],"streaming":[11,14],"services.":[12],"For":[13],"movie":[15,51,86],"services":[16],"like":[17],"Netflix,":[18],"recommendation":[19,117],"essential":[22],"for":[23,53],"helping":[24],"users":[25,141],"find":[26],"new":[27,82],"movies":[28],"to":[29,43,77,103],"enjoy.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,70,99],"propose":[35],"a":[36,45,54,58,120,137],"deep":[37,75,96],"learning":[38,76,97],"approach":[39,102],"based":[40,56],"on":[41,57,81,132],"autoencoders":[42],"produce":[44],"collaborative":[46,105],"filtering":[47,106],"system":[48,118],"which":[49,140],"predicts":[50],"ratings":[52,62,80,134],"user":[55],"large":[59],"database":[60],"from":[63,145],"other":[64],"users.":[65],"Using":[66],"the":[67,72,90],"MovieLens":[68],"dataset,":[69],"explore":[71],"use":[73],"predict":[78],"users'":[79],"movies,":[83],"thereby":[84],"enabling":[85],"recommendations.":[87],"To":[88],"verify":[89],"novelty":[91],"and":[92,109,135],"accuracy":[93],"our":[95,101,116],"approach,":[98],"compare":[100],"standard":[104],"techniques:":[107],"k-nearest-neighbor":[108],"matrix-factorization.":[110],"The":[111],"experimental":[112],"results":[113],"show":[114],"that":[115],"outperforms":[119],"user-based":[121],"neighborhood":[122],"baseline":[123],"both":[124,146],"terms":[126],"root":[128],"mean":[129],"squared":[130],"error":[131],"predicted":[133],"survey":[138],"judge":[142],"between":[143],"recommendations":[144],"systems.":[147]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2887332304","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":2}],"updated_date":"2025-01-23T04:03:31.801097","created_date":"2018-08-22"}