{"id":"https://openalex.org/W4386730171","doi":"https://doi.org/10.1145/3604915.3610660","title":"A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings","display_name":"A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings","publication_year":2023,"publication_date":"2023-09-14","ids":{"openalex":"https://openalex.org/W4386730171","doi":"https://doi.org/10.1145/3604915.3610660"},"language":"en","primary_location":{"id":"doi:10.1145/3604915.3610660","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3604915.3610660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050410519","display_name":"Amit Kumar Jaiswal","orcid":"https://orcid.org/0000-0001-8848-7041"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Amit Kumar Jaiswal","raw_affiliation_strings":["Surrey Business School, University of Surrey, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-8848-7041","affiliations":[{"raw_affiliation_string":"Surrey Business School, University of Surrey, United Kingdom","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070277096","display_name":"Yu Xiong","orcid":"https://orcid.org/0000-0001-7170-6201"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yu Xiong","raw_affiliation_strings":["Surrey Business School, University of Surrey, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-7170-6201","affiliations":[{"raw_affiliation_string":"Surrey Business School, University of Surrey, United Kingdom","institution_ids":["https://openalex.org/I28290843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28290843"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1190","last_page":"1195"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13274","display_name":"Expert finding and Q&A systems","score":0.9652000069618225,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8427135944366455},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7089591026306152},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6903615593910217},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6249761581420898},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5620943307876587},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5337268710136414},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.514800488948822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5083670020103455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49760058522224426},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.49534717202186584},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4575689136981964},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42722517251968384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8427135944366455},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7089591026306152},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6903615593910217},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6249761581420898},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5620943307876587},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5337268710136414},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.514800488948822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5083670020103455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49760058522224426},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.49534717202186584},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4575689136981964},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42722517251968384},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3604915.3610660","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3604915.3610660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2475334473","https://openalex.org/W2509893387","https://openalex.org/W2517540742","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2793768763","https://openalex.org/W2945772520","https://openalex.org/W2962745591","https://openalex.org/W2963924287","https://openalex.org/W2964182926","https://openalex.org/W2982902390","https://openalex.org/W2997130580","https://openalex.org/W2997891818","https://openalex.org/W3035635264","https://openalex.org/W3040478789","https://openalex.org/W3093945404","https://openalex.org/W3104030692","https://openalex.org/W3104307750","https://openalex.org/W3106252282","https://openalex.org/W3153687269","https://openalex.org/W3155541141","https://openalex.org/W3162929652"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W3048601286","https://openalex.org/W2965925734"],"abstract_inverted_index":{"Item":[0],"representation":[1,23,35],"holds":[2],"significant":[3,158],"importance":[4],"in":[5,74],"recommendation":[6,91,122,163],"systems,":[7],"which":[8],"encompasses":[9],"domains":[10],"such":[11,44],"as":[12,45,104],"news,":[13],"retail,":[14],"and":[15,18,47,115,147],"videos.":[16],"Retrieval":[17],"ranking":[19],"models":[20,123],"utilise":[21],"item":[22,65,100,153],"to":[24,57],"capture":[25],"the":[26,63,109,130,166],"user-item":[27],"relationship":[28],"based":[29],"on":[30,40,133],"user":[31,60,76],"behaviours.":[32],"While":[33],"existing":[34,121],"learning":[36,111,149],"methods":[37,52,69],"primarily":[38],"focus":[39],"optimising":[41],"item-based":[42,114],"mechanisms,":[43],"attention":[46],"sequential":[48],"modelling.":[49],"However,":[50],"these":[51,68],"lack":[53],"a":[54,85,93],"modelling":[55],"mechanism":[56],"directly":[58,97],"reflect":[59],"interests":[61,77],"within":[62],"learned":[64],"representations.":[66,101,117,154],"Consequently,":[67],"may":[70],"be":[71],"less":[72],"effective":[73],"capturing":[75],"indirectly.":[78],"To":[79],"address":[80],"this":[81],"challenge,":[82],"we":[83],"propose":[84],"novel":[86],"Interest-aware":[87],"Capsule":[88],"network":[89],"(IaCN)":[90],"model,":[92],"model-agnostic":[94],"framework":[95,119],"that":[96],"learns":[98],"interest-oriented":[99,152],"IaCN":[102],"serves":[103],"an":[105],"auxiliary":[106],"task,":[107],"enabling":[108],"joint":[110,148],"of":[112,151,168],"both":[113],"interest-based":[116],"This":[118],"adopts":[120],"without":[124],"requiring":[125],"substantial":[126],"redesign.":[127],"We":[128],"evaluate":[129],"proposed":[131],"approach":[132],"benchmark":[134],"datasets,":[135],"exploring":[136],"various":[137],"scenarios":[138],"involving":[139],"different":[140],"deep":[141],"neural":[142],"networks,":[143],"behaviour":[144],"sequence":[145],"lengths,":[146],"ratios":[150],"Experimental":[155],"results":[156],"demonstrate":[157],"performance":[159],"enhancements":[160],"across":[161],"diverse":[162],"models,":[164],"validating":[165],"effectiveness":[167],"our":[169],"approach.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
