{"id":"https://openalex.org/W4416035282","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1668","title":"A Text-Based Recommender System that Leverages Explicit Affective State Preferences","display_name":"A Text-Based Recommender System that Leverages Explicit Affective State Preferences","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416035282","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1668"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1668","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1668","pdf_url":"https://aclanthology.org/2025.emnlp-main.1668.pdf","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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-main.1668.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044409349","display_name":"Tonmoy Hasan","orcid":"https://orcid.org/0000-0002-2116-6822"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tonmoy Hasan","raw_affiliation_strings":["Department of Computer Science University of North Carolina at Charlotte"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020435927","display_name":"R\u0103zvan Bunescu","orcid":"https://orcid.org/0000-0003-2919-3566"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Razvan Bunescu","raw_affiliation_strings":["Department of Computer Science University of North Carolina at Charlotte"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044409349"],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37596154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"32839","last_page":"32853"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.14069999754428864,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.14069999754428864,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.09030000120401382,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.07509999722242355,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5564000010490417},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4821999967098236},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2955000102519989},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2793000042438507},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.26809999346733093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029999852180481},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.1668","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1668","pdf_url":"https://aclanthology.org/2025.emnlp-main.1668.pdf","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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1668","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1668","pdf_url":"https://aclanthology.org/2025.emnlp-main.1668.pdf","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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416035282.pdf","grobid_xml":"https://content.openalex.org/works/W4416035282.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"affective":[1,17,39,64,84,98,115,125,157,173],"attitude":[2],"of":[3,16,63,91,96,124,136,152,163],"liking":[4],"a":[5,13,52,59,88,109,161],"recommended":[6,154],"item":[7],"reflects":[8],"just":[9],"one":[10],"category":[11],"in":[12,71],"wide":[14],"spectrum":[15],"phenomena":[18],"that":[19,56,100,112],"also":[20],"includes":[21],"emotions":[22],"such":[23,29,41,114],"as":[24,30,34,36,42,117],"entranced":[25],"or":[26,32],"intrigued,":[27],"moods":[28],"cheerful":[31],"buoyant,":[33],"well":[35],"more":[37],"fine-grained":[38,97],"states,":[40],"\"pleasantly":[43],"surprised":[44],"by":[45,68],"the":[46,69,121,130,150],"conclusion\".In":[47],"this":[48],"paper,":[49],"we":[50,86],"introduce":[51],"novel":[53],"recommendation":[54,147],"task":[55,151],"can":[57],"leverage":[58,171],"virtually":[60],"unbounded":[61],"range":[62,162],"states":[65,99,126],"sought":[66],"explicitly":[67],"user":[70,92],"order":[72],"to":[73,81,142,170],"identify":[74],"items":[75,155],"that,":[76],"upon":[77],"consumption,":[78],"are":[79,101],"likely":[80],"induce":[82],"those":[83],"states.Correspondingly,":[85],"create":[87],"large":[89],"dataset":[90,123],"preferences":[93],"containing":[94],"expressions":[95,116],"mined":[102],"from":[103],"book":[104,137],"reviews,":[105,141],"and":[106,133,140,144],"propose":[107],"ACRec,":[108],"Transformer-based":[110],"architecture":[111],"leverages":[113],"input.We":[118],"then":[119],"use":[120],"resulting":[122],"preferences,":[127],"together":[128],"with":[129,156,160],"linked":[131],"users":[132],"their":[134],"histories":[135],"readings,":[138],"ratings,":[139],"train":[143],"evaluate":[145],"multiple":[146],"models":[148],"on":[149],"matching":[153],"preferences.Experimental":[158],"comparisons":[159],"state-of-the-art":[164],"baselines":[165],"demonstrate":[166],"ACRec's":[167],"superior":[168],"ability":[169],"explicit":[172],"preferences.":[174]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-08T00:00:00"}