{"id":"https://openalex.org/W4412673526","doi":"https://doi.org/10.1145/3731120.3744621","title":"WaveRec: Is Wavelet Transform a Better Alternative to Fourier Transform for Sequential Recommendation?","display_name":"WaveRec: Is Wavelet Transform a Better Alternative to Fourier Transform for Sequential Recommendation?","publication_year":2025,"publication_date":"2025-07-18","ids":{"openalex":"https://openalex.org/W4412673526","doi":"https://doi.org/10.1145/3731120.3744621"},"language":"en","primary_location":{"id":"doi:10.1145/3731120.3744621","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731120.3744621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)","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/A5119079166","display_name":"Byungmoon Heo","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Byungmoon Heo","raw_affiliation_strings":["Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100652679","display_name":"Jaekwang Kim","orcid":"https://orcid.org/0000-0001-5174-0074"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaekwang Kim","raw_affiliation_strings":["Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea and Convergence Program for Social Innovation, Sungkyunkwan University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea and Convergence Program for Social Innovation, Sungkyunkwan University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5119079166"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":3.0965,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92282729,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"497","last_page":"502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.998199999332428,"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":0.998199999332428,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9948999881744385,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9943000078201294,"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":0.6551737785339355},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.6381649971008301},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6157054305076599},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5277652740478516},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.45587509870529175},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.43083032965660095},{"id":"https://openalex.org/keywords/continuous-wavelet-transform","display_name":"Continuous wavelet transform","score":0.42976728081703186},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.3856008052825928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3702930212020874},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.3341316282749176},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32110795378685},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26197320222854614},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.17112812399864197},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.05531325936317444}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551737785339355},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.6381649971008301},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6157054305076599},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5277652740478516},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.45587509870529175},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.43083032965660095},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.42976728081703186},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.3856008052825928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3702930212020874},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3341316282749176},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32110795378685},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26197320222854614},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.17112812399864197},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.05531325936317444}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3731120.3744621","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731120.3744621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1246254620","display_name":null,"funder_award_id":"IITP-2025-RS-2024-00346737","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G5430176870","display_name":null,"funder_award_id":"IITP-2025-RS-2023-00259497, IITP-2025-RS-2023-00254129","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2098914003","https://openalex.org/W2593390416","https://openalex.org/W2605350416","https://openalex.org/W2783272285","https://openalex.org/W2963367478","https://openalex.org/W2984100107","https://openalex.org/W3021986761","https://openalex.org/W3022908749","https://openalex.org/W3065542300","https://openalex.org/W3081170586","https://openalex.org/W3100260481","https://openalex.org/W3133849783","https://openalex.org/W3206127589","https://openalex.org/W4220974940","https://openalex.org/W4255567990","https://openalex.org/W4384655737","https://openalex.org/W4393153637","https://openalex.org/W6605363982"],"related_works":["https://openalex.org/W2118155316","https://openalex.org/W2382218334","https://openalex.org/W2090697566","https://openalex.org/W2116983948","https://openalex.org/W2155549970","https://openalex.org/W2101997086","https://openalex.org/W2094847008","https://openalex.org/W3036712776","https://openalex.org/W4205424440","https://openalex.org/W2135037902"],"abstract_inverted_index":{"Recently,":[0],"sequential":[1,85],"recommendation":[2],"models":[3,150],"leverage":[4],"deep":[5],"neural":[6],"networks,":[7],"including":[8],"RNN,":[9],"CNN,":[10],"and":[11,60],"Transformer,":[12],"to":[13,37,117],"effectively":[14],"capture":[15],"user":[16,71,125],"preferences":[17],"from":[18],"behavioral":[19],"data.":[20],"User":[21],"behavior":[22,126],"sequences":[23],"inherently":[24],"contain":[25],"noise,":[26],"which":[27,54,88],"is":[28,63,179],"often":[29],"addressed":[30],"through":[31],"the":[32,45,51,143,160,169],"use":[33],"of":[34,58,91,162],"filtering":[35,42],"algorithms":[36,43],"mitigate":[38],"its":[39],"effects.":[40],"Generally,":[41],"utilize":[44],"Fourier":[46,52,144],"transform":[47,83,93,99,121,145],"for":[48,84,101,174],"processing.":[49],"However,":[50],"transform,":[53,153],"relies":[55],"on":[56,112],"combinations":[57],"sine":[59],"cosine":[61],"waves,":[62],"not":[64],"entirely":[65],"effective":[66],"in":[67,147],"handling":[68],"diverse":[69],"real-world":[70,114],"sequences.":[72],"To":[73],"address":[74],"this":[75],"limitation,":[76],"we":[77,141,154],"propose":[78],"a":[79],"method":[80],"called":[81],"Wavelet":[82,98],"Recommendation":[86],"(WaveRec),":[87],"takes":[89],"advantage":[90],"wavelet":[92,120,152,171],"as":[94],"an":[95],"alternative":[96],"approach.":[97,164],"allows":[100],"detailed":[102],"frequency":[103],"decomposition":[104],"by":[105],"using":[106],"various":[107],"filters.":[108],"We":[109,165],"conduct":[110],"experiments":[111],"four":[113,136],"benchmark":[115],"datasets":[116],"demonstrate":[118],"that":[119,132,168],"better":[122],"captures":[123],"complex":[124],"patterns.":[127],"The":[128],"experimental":[129],"results":[130],"show":[131],"our":[133,163],"model":[134],"outperforms":[135],"baseline":[137],"methods.":[138],"Furthermore,":[139],"when":[140],"replace":[142],"component":[146],"existing":[148],"state-of-the-art":[149],"with":[151],"observe":[155],"additional":[156],"performance":[157],"improvements,":[158],"underscoring":[159],"effectiveness":[161],"also":[166],"discover":[167],"appropriate":[170],"filter":[172],"varies":[173],"each":[175],"dataset.":[176],"Our":[177],"code":[178],"available":[180],"at":[181],"https://github.com/Byungmoon-Heo/WaveRec.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
