{"id":"https://openalex.org/W2583674722","doi":"https://doi.org/10.1145/3018661.3018689","title":"Recurrent Recommender Networks","display_name":"Recurrent Recommender Networks","publication_year":2017,"publication_date":"2017-02-02","ids":{"openalex":"https://openalex.org/W2583674722","doi":"https://doi.org/10.1145/3018661.3018689","mag":"2583674722"},"language":"en","primary_location":{"id":"doi:10.1145/3018661.3018689","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018689","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018689&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3018689&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023917342","display_name":"Chao-Yuan Wu","orcid":"https://orcid.org/0000-0002-5690-8865"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chao-Yuan Wu","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009154893","display_name":"Amr Ahmed","orcid":"https://orcid.org/0000-0002-7749-7911"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amr Ahmed","raw_affiliation_strings":["Google, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080988309","display_name":"Alex Beutel","orcid":"https://orcid.org/0000-0002-5917-2849"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Beutel","raw_affiliation_strings":["Google, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000245150","display_name":"Alexander J. Smola","orcid":"https://orcid.org/0000-0002-7963-4721"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander J. Smola","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063598775","display_name":"How Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"How Jing","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5023917342"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":169.9685,"has_fulltext":true,"cited_by_count":735,"citation_normalized_percentile":{"value":0.99974832,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"495","last_page":"503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9980999827384949,"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":0.9980999827384949,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9740999937057495,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8352789878845215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8217384815216064},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6243042349815369},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.5455893278121948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.532355546951294},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.5131571888923645},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4911174476146698},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.479148805141449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4772765040397644},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.47686895728111267},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.45278283953666687},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4248994290828705},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19276422262191772},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.11043059825897217},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.094776451587677},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0915209949016571}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8352789878845215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8217384815216064},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6243042349815369},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.5455893278121948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.532355546951294},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.5131571888923645},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4911174476146698},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.479148805141449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4772765040397644},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.47686895728111267},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.45278283953666687},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4248994290828705},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19276422262191772},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.11043059825897217},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.094776451587677},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0915209949016571},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3018661.3018689","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018689","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018689&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3018661.3018689","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018689","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018689&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G2180245026","display_name":null,"funder_award_id":"1252522","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G255524836","display_name":null,"funder_award_id":"DGE-1252522","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3116621514","display_name":"III: Medium: Collaborative Research: Collective Opinion Fraud Detection: Identifying and Integrating Cues from Language, Behavior, and Networks","funder_award_id":"1408924","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3968667353","display_name":null,"funder_award_id":"IIS-1408924","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6894402473","display_name":null,"funder_award_id":"Fellowship","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2583674722.pdf","grobid_xml":"https://content.openalex.org/works/W2583674722.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1720514416","https://openalex.org/W1788180225","https://openalex.org/W2020098476","https://openalex.org/W2042281163","https://openalex.org/W2064675550","https://openalex.org/W2080320419","https://openalex.org/W2088155659","https://openalex.org/W2103139809","https://openalex.org/W2110798204","https://openalex.org/W2127411301","https://openalex.org/W2135194391","https://openalex.org/W2137245235","https://openalex.org/W2138153286","https://openalex.org/W2142972908","https://openalex.org/W2169240294","https://openalex.org/W2171813380","https://openalex.org/W2186615578","https://openalex.org/W2262817822","https://openalex.org/W2274757334","https://openalex.org/W2341535507","https://openalex.org/W2515520555","https://openalex.org/W2950741027","https://openalex.org/W6638233953","https://openalex.org/W6676481782","https://openalex.org/W6680315907","https://openalex.org/W6703949738","https://openalex.org/W6992987501","https://openalex.org/W7070499600"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W2735929803","https://openalex.org/W4220714703","https://openalex.org/W1484355083","https://openalex.org/W3008845055","https://openalex.org/W2098758514","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622","https://openalex.org/W2556532874"],"abstract_inverted_index":{"Recommender":[0,45],"systems":[1],"traditionally":[2],"assume":[3],"that":[4,17,48,73],"user":[5],"profiles":[6],"and":[7,63,94],"movie":[8],"attributes":[9],"are":[10,14,20,24,49],"static.":[11],"Temporal":[12],"dynamics":[13],"purely":[15],"reactive,":[16],"is,":[18],"they":[19,23],"inferred":[21],"after":[22,27],"observed,":[25],"e.g.":[26],"a":[28,66,79],"user's":[29],"taste":[30],"has":[31],"changed":[32],"or":[33],"based":[34],"on":[35],"hand-engineered":[36],"temporal":[37],"bias":[38],"corrections":[39],"for":[40],"movies.":[41],"We":[42],"propose":[43],"Recurrent":[44],"Networks":[46],"(RRN)":[47],"able":[50],"to":[51,78],"predict":[52],"future":[53],"behavioral":[54],"trajectories.":[55],"This":[56],"is":[57,96],"achieved":[58],"by":[59],"endowing":[60],"both":[61],"users":[62],"movies":[64],"with":[65],"Long":[67],"Short-Term":[68],"Memory":[69],"(LSTM)":[70],"autoregressive":[71],"model":[72,89],"captures":[74],"dynamics,":[75],"in":[76],"addition":[77],"more":[80],"traditional":[81],"low-rank":[82],"factorization.":[83],"On":[84],"multiple":[85],"real-world":[86],"datasets,":[87],"our":[88],"offers":[90],"excellent":[91],"prediction":[92],"accuracy":[93],"it":[95],"very":[97],"compact,":[98],"since":[99],"we":[100],"need":[101],"not":[102],"learn":[103],"latent":[104],"state":[105,110],"but":[106],"rather":[107],"just":[108],"the":[109],"transition":[111],"function.":[112]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":43},{"year":2024,"cited_by_count":80},{"year":2023,"cited_by_count":71},{"year":2022,"cited_by_count":71},{"year":2021,"cited_by_count":127},{"year":2020,"cited_by_count":101},{"year":2019,"cited_by_count":129},{"year":2018,"cited_by_count":81},{"year":2017,"cited_by_count":25}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}