{"id":"https://openalex.org/W2474765392","doi":"https://doi.org/10.1145/2911451.2914726","title":"Multi-Rate Deep Learning for Temporal Recommendation","display_name":"Multi-Rate Deep Learning for Temporal Recommendation","publication_year":2016,"publication_date":"2016-07-07","ids":{"openalex":"https://openalex.org/W2474765392","doi":"https://doi.org/10.1145/2911451.2914726","mag":"2474765392"},"language":"en","primary_location":{"id":"doi:10.1145/2911451.2914726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2914726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","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/A5100688422","display_name":"Yang Song","orcid":"https://orcid.org/0000-0001-8252-9626"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":["Microsoft Research Redmond, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Redmond, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091764449","display_name":"Ali Elkahky","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Mamdouh Elkahky","raw_affiliation_strings":["Columbia University, New York City, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York City, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101727205","display_name":"Xiaodong He","orcid":"https://orcid.org/0000-0002-9463-9168"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodong He","raw_affiliation_strings":["Microsoft Research Redmond, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Redmond, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":56.834,"has_fulltext":false,"cited_by_count":173,"citation_normalized_percentile":{"value":0.99880451,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"909","last_page":"912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.991599977016449,"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/T11309","display_name":"Music and Audio Processing","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.8508102893829346},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7598971128463745},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7102415561676025},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6327168345451355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.620254635810852},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5965297818183899},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5285719037055969},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4972722828388214},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46743717789649963},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4644695818424225},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.46036139130592346},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.45350292325019836},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4384101927280426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8508102893829346},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7598971128463745},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7102415561676025},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6327168345451355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.620254635810852},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5965297818183899},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5285719037055969},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4972722828388214},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46743717789649963},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4644695818424225},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46036139130592346},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.45350292325019836},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4384101927280426},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2911451.2914726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2914726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1500188831","https://openalex.org/W1976999215","https://openalex.org/W1994389483","https://openalex.org/W2013955429","https://openalex.org/W2041022395","https://openalex.org/W2057763140","https://openalex.org/W2072992969","https://openalex.org/W2073013176","https://openalex.org/W2099866409","https://openalex.org/W2100495367","https://openalex.org/W2101409192","https://openalex.org/W2110798204","https://openalex.org/W2114079787","https://openalex.org/W2128853364","https://openalex.org/W2136189984","https://openalex.org/W2137028279","https://openalex.org/W2153111836","https://openalex.org/W2153579005","https://openalex.org/W2158978451","https://openalex.org/W2262817822","https://openalex.org/W2273452082","https://openalex.org/W2405923393","https://openalex.org/W2604272474","https://openalex.org/W2950133940","https://openalex.org/W6693983917"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W4246980185","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W3125580266","https://openalex.org/W44246808","https://openalex.org/W4317039510","https://openalex.org/W3128438030"],"abstract_inverted_index":{"Modeling":[0],"temporal":[1,19,52,80],"behavior":[2,53],"in":[3,54],"recommendation":[4,57,86,124],"systems":[5],"is":[6,47,113],"an":[7],"important":[8],"and":[9,27,78,134],"challenging":[10],"problem.":[11],"Its":[12],"challenges":[13],"come":[14],"from":[15,120],"the":[16,22,38,41,73,85,90,104,135,143],"fact":[17],"that":[18,71,139],"modeling":[20],"increases":[21],"cost":[23],"of":[24,33,75,106,131],"parameter":[25],"estimation":[26],"inference,":[28],"while":[29],"requiring":[30],"large":[31],"amount":[32],"data":[34,118],"to":[35,50,83,102,115,128],"reliably":[36],"learn":[37],"model":[39,51,91,112],"with":[40],"additional":[42],"time":[43],"dimensions.":[44],"Therefore,":[45],"it":[46],"often":[48],"difficult":[49],"large-scale":[55,94],"real-world":[56,117],"systems.":[58],"In":[59],"this":[60],"work,":[61],"we":[62,96],"propose":[63,97],"a":[64,98,116,121,129],"novel":[65,99],"deep":[66],"neural":[67],"network":[68],"based":[69],"architecture":[70],"models":[72],"combination":[74],"long-term":[76],"static":[77],"short-term":[79],"user":[81],"preferences":[82],"improve":[84],"performance.":[87],"To":[88],"train":[89],"efficiently":[92],"for":[93],"applications,":[95],"pre-train":[100],"method":[101,141],"reduce":[103],"number":[105],"free":[107],"parameters":[108],"significantly.":[109,145],"The":[110],"resulted":[111],"applied":[114],"set":[119,130],"commercial":[122],"News":[123],"system.":[125],"We":[126],"compare":[127],"established":[132],"baselines":[133],"experimental":[136],"results":[137],"show":[138],"our":[140],"outperforms":[142],"state-of-the-art":[144]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":35},{"year":2018,"cited_by_count":28},{"year":2017,"cited_by_count":18},{"year":2016,"cited_by_count":3}],"updated_date":"2026-07-11T18:08:03.149640","created_date":"2025-10-10T00:00:00"}