{"id":"https://openalex.org/W4392084141","doi":"https://doi.org/10.1145/3640912.3640983","title":"Recurrent Attentive Neural Networks for Sequential Recommendation","display_name":"Recurrent Attentive Neural Networks for Sequential Recommendation","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4392084141","doi":"https://doi.org/10.1145/3640912.3640983"},"language":"en","primary_location":{"id":"doi:10.1145/3640912.3640983","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640912.3640983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Communication Network and Machine Learning","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/A5101998638","display_name":"Lei Tan","orcid":"https://orcid.org/0000-0001-7469-1025"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lei Tan","raw_affiliation_strings":["Henan Key Laboratory of Cyberspace Situation Awareness, China"],"raw_orcid":"https://orcid.org/0000-0001-7469-1025","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Cyberspace Situation Awareness, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069708005","display_name":"Jinmao Xu","orcid":"https://orcid.org/0000-0003-1152-3969"},"institutions":[{"id":"https://openalex.org/I4210094772","display_name":"Henan University of Engineering","ror":"https://ror.org/007wym039","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210094772"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinmao Xu","raw_affiliation_strings":["Henan University of Engineering, China"],"raw_orcid":"https://orcid.org/0000-0003-1152-3969","affiliations":[{"raw_affiliation_string":"Henan University of Engineering, China","institution_ids":["https://openalex.org/I4210094772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087837624","display_name":"Daofu Gong","orcid":"https://orcid.org/0000-0002-7810-2950"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daofu Gong","raw_affiliation_strings":["Henan Key Laboratory of Cyberspace Situation Awareness, China"],"raw_orcid":"https://orcid.org/0000-0002-7810-2950","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Cyberspace Situation Awareness, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034700690","display_name":"Fenlin Liu","orcid":"https://orcid.org/0000-0001-8019-1713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fenlin Liu","raw_affiliation_strings":["Henan Key Laboratory of Cyberspace Situation Awareness, China"],"raw_orcid":"https://orcid.org/0000-0001-8019-1713","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Cyberspace Situation Awareness, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101998638"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3453,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.86656544,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"356","last_page":"360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.95660001039505,"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":0.95660001039505,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7484540939331055},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5262067914009094},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4985780715942383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4915235936641693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32594239711761475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7484540939331055},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5262067914009094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4985780715942383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4915235936641693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32594239711761475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3640912.3640983","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640912.3640983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Communication Network and Machine Learning","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":12,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2608239929","https://openalex.org/W2903296288","https://openalex.org/W2989395196","https://openalex.org/W3041537557","https://openalex.org/W4301113013","https://openalex.org/W6600586173","https://openalex.org/W6600686112","https://openalex.org/W6600704668","https://openalex.org/W6601449391","https://openalex.org/W6601574642","https://openalex.org/W6823560597"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Sequential":[0],"recommendation":[1,23],"is":[2,41,89,125],"essential":[3],"in":[4,33],"modern":[5],"online":[6],"service":[7],"platforms.":[8],"By":[9],"modeling":[10],"the":[11,18,27,31,34,70,74,84,93,108,112,117,121,129,134,159,163],"evolving":[12],"preferences":[13,47],"of":[14,30,116,165],"a":[15,58,149],"user":[16,32,46,71,140],"from":[17,73],"historical":[19,50],"behavior":[20,51],"sequence,":[21],"sequential":[22,38,66,81,114,154],"aims":[24],"to":[25,43,91,127],"predict":[26],"next":[28],"interaction":[29],"near":[35],"future.":[36],"For":[37],"recommendation,":[39,67],"it":[40],"challenging":[42],"comprehensively":[44],"characterize":[45],"based":[48],"on":[49,158],"sequences.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"propose":[57],"Recurrent":[59],"Attentive":[60],"Neural":[61],"Networks":[62],"model":[63],"(RANN)":[64],"for":[65,103,153],"which":[68,106],"characterizes":[69],"preference":[72,110,141],"long-term":[75,109],"preference,":[76],"short-term":[77,86,130],"interest,":[78],"and":[79,96,111,143],"current":[80,113],"pattern.":[82],"Specifically,":[83],"long":[85],"memory":[87],"network":[88,152],"employed":[90],"generate":[92],"cell":[94],"state":[95,98],"hidden":[97,137],"at":[99],"each":[100,104],"time":[101],"step":[102],"user,":[105,118],"represent":[107],"pattern":[115],"respectively.":[119],"And":[120],"scaled":[122],"dot-product":[123],"attention":[124],"utilized":[126],"capture":[128],"interest":[131],"feature":[132],"among":[133],"most":[135],"recent":[136],"states.":[138],"Finally,":[139],"features":[142],"item":[144],"embeddings":[145],"are":[146],"integrated":[147],"into":[148],"next-item":[150],"prediction":[151],"recommendation.":[155],"Experimental":[156],"results":[157],"real-world":[160],"dataset":[161],"verify":[162],"superiority":[164],"RANN":[166],"against":[167],"state-of-the-art":[168],"baselines.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
