{"id":"https://openalex.org/W4384655737","doi":"https://doi.org/10.1145/3539618.3591689","title":"Frequency Enhanced Hybrid Attention Network for Sequential Recommendation","display_name":"Frequency Enhanced Hybrid Attention Network for Sequential Recommendation","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384655737","doi":"https://doi.org/10.1145/3539618.3591689"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3591689","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591689","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th 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/A5100625072","display_name":"Xinyu Du","orcid":"https://orcid.org/0000-0002-7807-0063"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyu Du","raw_affiliation_strings":["Soochow University, SuZhou, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073737476","display_name":"Huanhuan Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanhuan Yuan","raw_affiliation_strings":["Soochow University, SuZhou, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016091616","display_name":"Pengpeng Zhao","orcid":"https://orcid.org/0000-0001-6721-6576"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengpeng Zhao","raw_affiliation_strings":["Soochow University, SuZhou, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008653507","display_name":"Jianfeng Qu","orcid":"https://orcid.org/0000-0002-3212-7618"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianfeng Qu","raw_affiliation_strings":["Soochow University, SuZhou, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041921496","display_name":"Fuzhen Zhuang","orcid":"https://orcid.org/0000-0001-9170-7009"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuzhen Zhuang","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070515519","display_name":"Guanfeng Liu","orcid":"https://orcid.org/0000-0001-8980-4950"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Guanfeng Liu","raw_affiliation_strings":["Macquarie University, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114706253","display_name":"Yanchi Liu","orcid":"https://orcid.org/0000-0002-8187-6762"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanchi Liu","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051706630","display_name":"Victor S. Sheng","orcid":"https://orcid.org/0000-0003-4960-174X"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Victor S. Sheng","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100625072"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":44.2949,"has_fulltext":false,"cited_by_count":97,"citation_normalized_percentile":{"value":0.99881598,"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":"78","last_page":"88"},"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":0.9957000017166138,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9835000038146973,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8174843788146973},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.715336799621582},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5902432799339294},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5117177963256836},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4729219079017639},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4643822908401489},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4419686496257782},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.4409034252166748},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4292592704296112},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3452417254447937},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09798616170883179},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08065718412399292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8174843788146973},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.715336799621582},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5902432799339294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5117177963256836},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4729219079017639},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4643822908401489},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4419686496257782},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4409034252166748},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4292592704296112},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3452417254447937},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09798616170883179},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08065718412399292},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3591689","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591689","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1730154324","display_name":null,"funder_award_id":"61876117","funder_id":"https://openalex.org/F4320327518","funder_display_name":"Priority Academic Program Development of Jiangsu Higher Education Institutions"},{"id":"https://openalex.org/G2891303054","display_name":null,"funder_award_id":"62176175","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4039119761","display_name":null,"funder_award_id":"61876117, 62176175","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4875783979","display_name":null,"funder_award_id":"61876117","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8626528583","display_name":null,"funder_award_id":"202213","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320327518","display_name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1509559802","https://openalex.org/W1995771729","https://openalex.org/W2020141429","https://openalex.org/W2061171222","https://openalex.org/W2102182691","https://openalex.org/W2139317017","https://openalex.org/W2140310134","https://openalex.org/W2152202842","https://openalex.org/W2154136024","https://openalex.org/W2524638710","https://openalex.org/W2746011824","https://openalex.org/W2783272285","https://openalex.org/W2950416221","https://openalex.org/W2966483207","https://openalex.org/W2984100107","https://openalex.org/W2996931760","https://openalex.org/W2997329666","https://openalex.org/W3005071803","https://openalex.org/W3034771037","https://openalex.org/W3047391881","https://openalex.org/W3088257568","https://openalex.org/W3092923133","https://openalex.org/W3096934581","https://openalex.org/W3097021616","https://openalex.org/W3100260481","https://openalex.org/W3103556460","https://openalex.org/W3105661801","https://openalex.org/W3106346472","https://openalex.org/W3156977692","https://openalex.org/W3173539742","https://openalex.org/W3174864715","https://openalex.org/W3194671304","https://openalex.org/W3195554589","https://openalex.org/W3195830874","https://openalex.org/W3197308578","https://openalex.org/W3197407685","https://openalex.org/W3199003182","https://openalex.org/W3206127589","https://openalex.org/W3209225889","https://openalex.org/W3212890323","https://openalex.org/W3214773515","https://openalex.org/W4220974940","https://openalex.org/W4224315226","https://openalex.org/W4224316819","https://openalex.org/W4225494949","https://openalex.org/W4226158691","https://openalex.org/W4287022992","https://openalex.org/W6739901393","https://openalex.org/W6754159242","https://openalex.org/W6774403207","https://openalex.org/W6782014738","https://openalex.org/W6785302134","https://openalex.org/W6790680487","https://openalex.org/W6795146805","https://openalex.org/W6797578546","https://openalex.org/W6810653034"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W1947057263","https://openalex.org/W2355447608","https://openalex.org/W1980067354","https://openalex.org/W2033324903","https://openalex.org/W1978333447"],"abstract_inverted_index":{"The":[0],"self-attention":[1,32,107],"mechanism,":[2],"which":[3],"equips":[4],"with":[5,55,112],"a":[6,85,113,134,158],"strong":[7],"capability":[8],"of":[9,15],"modeling":[10],"long-range":[11],"dependencies,":[12],"is":[13],"one":[14],"the":[16,21,47,50,64,69,77,80,103,109,141,146,151,179,196,203],"extensively":[17],"used":[18,191],"techniques":[19],"in":[20,49,68,108,127,140,157,177],"sequential":[22],"recommendation":[23],"field.":[24],"However,":[25],"many":[26],"recent":[27],"studies":[28],"represent":[29],"that":[30,172,195],"current":[31],"based":[33],"models":[34,59],"are":[35,39,53,60,168,175],"low-pass":[36],"filters":[37],"and":[38,83,120,149,153,165,182],"inadequate":[40],"to":[41,62,79,116,144,170],"capture":[42,145],"high-frequency":[43,121],"information.":[44],"Furthermore,":[45],"since":[46],"items":[48],"user":[51],"behaviors":[52],"intertwined":[54],"each":[56],"other,":[57],"these":[58],"incomplete":[61],"distinguish":[63],"inherent":[65],"periodicity":[66],"obscured":[67],"time":[70,105,152,180],"domain.":[71,184],"In":[72,97],"this":[73,98],"work,":[74],"we":[75,100,131],"shift":[76],"perspective":[78],"frequency":[81,110,142,154,166,183],"domain,":[82],"propose":[84],"novel":[86],"Frequency":[87],"Enhanced":[88],"Hybrid":[89],"Attention":[90],"Network":[91],"for":[92],"Sequential":[93],"Recommendation,":[94],"namely":[95],"FEARec.":[96],"model,":[99],"firstly":[101],"improve":[102],"original":[104],"domain":[106,111,143,181],"ramp":[114],"structure":[115],"make":[117],"both":[118,162,178],"low-frequency":[119],"information":[122],"could":[123],"be":[124],"explicitly":[125],"learned":[126],"our":[128],"approach.":[129],"Moreover,":[130],"additionally":[132],"design":[133],"similar":[135],"attention":[136,156],"mechanism":[137],"via":[138],"auto-correlation":[139],"periodic":[147],"characteristics":[148],"fuse":[150],"level":[155],"union":[159],"model.":[160],"Finally,":[161],"contrastive":[163],"learning":[164],"regularization":[167],"utilized":[169],"ensure":[171],"multiple":[173],"views":[174],"aligned":[176],"Extensive":[185],"experiments":[186],"conducted":[187],"on":[188],"four":[189],"widely":[190],"benchmark":[192],"datasets":[193],"demonstrate":[194],"proposed":[197],"model":[198],"performs":[199],"significantly":[200],"better":[201],"than":[202],"state-of-the-art":[204],"approaches.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":59},{"year":2024,"cited_by_count":28}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}