{"id":"https://openalex.org/W7163425039","doi":"https://doi.org/10.48550/arxiv.2606.03718","title":"MARS: Multi-rate Aggregation of Recency Signals for Sequential Recommendation across Sparse and Dense Regimes","display_name":"MARS: Multi-rate Aggregation of Recency Signals for Sequential Recommendation across Sparse and Dense Regimes","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163425039","doi":"https://doi.org/10.48550/arxiv.2606.03718"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.03718","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137716848","display_name":"Zhenyu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137794567","display_name":"Shuigeng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shuigeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9223999977111816,"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":0.9223999977111816,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.010400000028312206,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.00860000029206276,"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/mars-exploration-program","display_name":"Mars Exploration Program","score":0.6184999942779541},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.46140000224113464},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.460999995470047},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.43369999527931213},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3799999952316284},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.358599990606308},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.3580000102519989},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.35359999537467957},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.35019999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938999891281128},{"id":"https://openalex.org/C83260615","wikidata":"https://www.wikidata.org/wiki/Q6773121","display_name":"Mars Exploration Program","level":2,"score":0.6184999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.46140000224113464},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.43369999527931213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40299999713897705},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C2776481522","wikidata":"https://www.wikidata.org/wiki/Q40218","display_name":"Orbiter","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3393999934196472},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3352999985218048},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3127000033855438},{"id":"https://openalex.org/C2778049214","wikidata":"https://www.wikidata.org/wiki/Q7512234","display_name":"Sigma","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2883000075817108},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sequential":[0],"recommenders":[1],"weight":[2],"historical":[3],"interactions":[4],"either":[5],"through":[6,13],"positional":[7],"self-attention":[8],"as":[9,19],"in":[10,20,66,193],"Transformers":[11],"or":[12],"a":[14,55,113],"single":[15],"implicit":[16],"decay":[17],"schedule":[18],"State-Space":[21],"Models.":[22],"Neither":[23],"makes":[24],"the":[25,94,99,119,131,159,164,171,186,194],"multi-scale":[26],"temporal":[27],"structure":[28],"of":[29,98,174],"real":[30,43],"user":[31],"behaviour":[32],"explicit.":[33],"We":[34],"propose":[35],"MARS,":[36],"an":[37],"encoder-agnostic":[38],"aggregation":[39],"operator":[40],"that":[41],"consumes":[42],"timestamps":[44],"and":[45,64,86,109,143,184],"produces":[46],"K":[47],"summaries":[48],"emphasising":[49],"distinct":[50],"recency":[51],"scales,":[52],"fused":[53],"by":[54,74],"context-adaptive":[56],"gate.":[57],"MARS":[58,69,117,175],"adds":[59],"at":[60,154,176],"most":[61],"6%":[62],"parameters":[63],"runs":[65],"$\\mathcal{O}(LdK)$":[67],"time.":[68],"adapts":[70],"to":[71,178],"data":[72,85,138,183],"density":[73],"automatically":[75],"selecting":[76],"between":[77],"two":[78],"encoder":[79],"instantiations:":[80],"MARS-T":[81],"(Transformer)":[82],"for":[83,89],"sparse":[84,137,182],"MARS-M":[87],"(Mamba)":[88],"dense":[90,152],"data,":[91],"based":[92],"on":[93,122,136,141,151,181],"average":[95],"sequence":[96],"length":[97],"training":[100],"set.":[101],"On":[102],"five":[103],"public":[104],"benchmarks":[105],"against":[106],"ten":[107],"Transformer-":[108],"Mamba-based":[110],"baselines":[111],"under":[112],"unified":[114],"RecBole":[115],"protocol,":[116],"attains":[118],"best":[120],"HR@10":[121,145,180],"every":[123],"benchmark,":[124],"with":[125],"mean":[126],"relative":[127],"gain":[128],"+19.7%":[129],"over":[130,149],"strongest":[132],"content-only":[133],"Transformer":[134],"baseline":[135],"(reaching":[139],"+36.2%":[140],"Games)":[142],"+3.2%":[144],"/":[146],"+0.9%":[147],"NDCG":[148],"SIGMA":[150],"ML-1M":[153],"42%":[155],"fewer":[156],"MFLOPs,":[157],"occupying":[158],"accuracy-efficiency":[160],"Pareto":[161],"frontier":[162],"across":[163],"data-density":[165],"spectrum.":[166],"A":[167],"backbone-only":[168],"ablation":[169],"isolates":[170],"marginal":[172],"contribution":[173],"+4%":[177],"+19%":[179],"motivates":[185],"dual-instantiation":[187],"design.":[188],"The":[189],"code":[190],"is":[191],"included":[192],"supplementary":[195],"material.":[196]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}