{"id":"https://openalex.org/W7155207909","doi":"https://doi.org/10.1145/3774935.3806187","title":"Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts","display_name":"Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7155207909","doi":"https://doi.org/10.1145/3774935.3806187"},"language":null,"primary_location":{"id":"doi:10.1145/3774935.3806187","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774935.3806187","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774935.3806187","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117539773","display_name":"Rafael T. Sereicikas","orcid":null},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael T. Sereicikas","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil"],"raw_orcid":"https://orcid.org/0009-0009-9198-5469","affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011004318","display_name":"Pedro R. Pires","orcid":"https://orcid.org/0000-0001-7990-9097"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Pedro R. Pires","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-7990-9097","affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098954543","display_name":"Greg\u00f3rio F. Azevedo","orcid":"https://orcid.org/0000-0002-1096-7456"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Greg\u00f3rio F. Azevedo","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-1096-7456","affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134062112","display_name":"Tiago A. Almeida","orcid":null},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Tiago A. Almeida","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-6943-8033","affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, Sorocaba, S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177909021"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40723601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.35359999537467957,"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":0.35359999537467957,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.09880000352859497,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.08129999786615372,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6972000002861023},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6363999843597412},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6305000185966492},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5580000281333923},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4799000024795532},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45399999618530273},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.43970000743865967},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.34599998593330383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7315999865531921},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6972000002861023},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6363999843597412},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6305000185966492},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49160000681877136},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45399999618530273},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3255999982357025},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26989999413490295},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.26260000467300415}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3774935.3806187","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774935.3806187","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2604.15581","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2604.15581","pdf_url":"https://arxiv.org/pdf/2604.15581","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3774935.3806187","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774935.3806187","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2823776494","display_name":null,"funder_award_id":"88887","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"},{"id":"https://openalex.org/G4374289430","display_name":null,"funder_award_id":"2023-00","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"},{"id":"https://openalex.org/G6191138830","display_name":null,"funder_award_id":"88887","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G6438247056","display_name":null,"funder_award_id":"2021/","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G7999242680","display_name":null,"funder_award_id":"2021/14591-7","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G8352907975","display_name":null,"funder_award_id":"311867/2023-5","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Effective":[0],"user":[1,27,57],"modeling":[2],"requires":[3],"distinguishing":[4],"between":[5,62],"short-term":[6],"and":[7,66,130,161],"long-term":[8],"preference":[9],"evolution.":[10],"While":[11],"item":[12,140],"embeddings":[13],"have":[14],"become":[15],"a":[16,78],"key":[17],"component":[18],"of":[19,56,80,176,181],"recommender":[20],"systems,":[21],"standard":[22],"approaches":[23,95],"like":[24],"Item2Vec":[25],"treat":[26],"histories":[28],"as":[29,41,44],"unordered":[30],"sets":[31],"(bag-of-items),":[32],"implicitly":[33],"assuming":[34],"that":[35,84,96,155],"interactions":[36,126],"separated":[37,46],"by":[38,47],"minutes":[39],"are":[40],"semantically":[42],"related":[43],"those":[45],"months.":[48],"This":[49],"simplification":[50],"flattens":[51],"the":[52,60,90,177],"rich":[53],"temporal":[54,86,106,146],"structure":[55],"behavior,":[58],"obscuring":[59],"distinction":[61],"coherent":[63],"consumption":[64],"sessions":[65],"gradual":[67],"interest":[68],"drifts.":[69],"In":[70],"this":[71],"work,":[72],"we":[73],"introduce":[74],"TAI2Vec":[75,101,156],"(Time-Aware":[76],"Item-to-Vector),":[77],"family":[79],"lightweight":[81],"embedding":[82],"models":[83],"integrates":[85],"proximity":[87],"directly":[88],"into":[89,127],"representation":[91],"learning":[92],"process.":[93],"Unlike":[94],"apply":[97],"global":[98],"time":[99],"constraints,":[100],"is":[102,188],"user-adaptive,":[103],"tailoring":[104],"its":[105],"definitions":[107],"to":[108,123,138,183],"individual":[109],"interaction":[110],"paces.":[111],"We":[112],"propose":[113],"two":[114],"complementary":[115],"strategies:":[116],"TAI2Vec-Disc,":[117],"which":[118,132],"utilizes":[119],"personalized":[120],"anomaly":[121],"detection":[122],"dynamically":[124],"segment":[125],"semantic":[128],"sessions,":[129],"TAI2Vec-Cont,":[131],"employs":[133],"continuous,":[134],"user-specific":[135],"decay":[136],"functions":[137],"weigh":[139],"relationships":[141],"based":[142],"on":[143],"their":[144],"relative":[145],"distance.":[147],"Experimental":[148],"results":[149],"across":[150],"eight":[151],"diverse":[152],"datasets":[153],"demonstrate":[154],"consistently":[157],"produces":[158],"more":[159],"accurate":[160],"behaviorally":[162],"grounded":[163],"representations":[164],"than":[165],"static":[166],"baselines,":[167],"achieving":[168],"competitive":[169],"or":[170],"superior":[171],"performance":[172],"in":[173],"over":[174],"80%":[175],"datasets,":[178],"with":[179],"improvements":[180],"up":[182],"135%.":[184],"The":[185],"source":[186],"code":[187],"publicly":[189],"available":[190],"at":[191],"https://github.com/UFSCar-LaSID/tai2vec.":[192]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2026-04-23T00:00:00"}