{"id":"https://openalex.org/W3083640775","doi":"https://doi.org/10.1145/3409256.3409835","title":"A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation","display_name":"A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation","publication_year":2020,"publication_date":"2020-09-05","ids":{"openalex":"https://openalex.org/W3083640775","doi":"https://doi.org/10.1145/3409256.3409835","mag":"3083640775"},"language":"en","primary_location":{"id":"doi:10.1145/3409256.3409835","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409256.3409835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.gla.ac.uk/219367/1/219367.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102858602","display_name":"Yaxiong Wu","orcid":"https://orcid.org/0000-0002-1860-0122"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yaxiong Wu","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057643560","display_name":"Craig Macdonald","orcid":"https://orcid.org/0000-0003-3143-279X"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Craig Macdonald","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079046603","display_name":"Iadh Ounis","orcid":"https://orcid.org/0000-0003-4701-3223"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Iadh Ounis","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102858602"],"corresponding_institution_ids":["https://openalex.org/I7882870"],"apc_list":null,"apc_paid":null,"fwci":3.3422,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.9364232,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"89","last_page":"96"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9800000190734863,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9751999974250793,"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/autoencoder","display_name":"Autoencoder","score":0.9016046524047852},{"id":"https://openalex.org/keywords/movielens","display_name":"MovieLens","score":0.7906844615936279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7540903687477112},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6219518184661865},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5624353885650635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5536827445030212},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5308906435966492},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5191490054130554},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.5153266787528992},{"id":"https://openalex.org/keywords/cold-start","display_name":"Cold start (automotive)","score":0.4871380031108856},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4810205101966858},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.42320379614830017},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4217899739742279},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.2830055356025696},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.22382423281669617},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12860974669456482},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10544395446777344}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9016046524047852},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.7906844615936279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7540903687477112},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6219518184661865},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5624353885650635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5536827445030212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5308906435966492},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5191490054130554},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.5153266787528992},{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.4871380031108856},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4810205101966858},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.42320379614830017},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4217899739742279},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.2830055356025696},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22382423281669617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12860974669456482},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10544395446777344},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3409256.3409835","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409256.3409835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.gla.ac.uk:219367","is_oa":true,"landing_page_url":"https://eprints.gla.ac.uk/view/author/55148.html>,","pdf_url":"https://eprints.gla.ac.uk/219367/1/219367.pdf","source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":{"id":"pmh:oai:eprints.gla.ac.uk:219367","is_oa":true,"landing_page_url":"https://eprints.gla.ac.uk/view/author/55148.html>,","pdf_url":"https://eprints.gla.ac.uk/219367/1/219367.pdf","source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1989121949","display_name":null,"funder_award_id":"EP/R018634/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5187265158","display_name":"Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics","funder_award_id":"EP/R018634/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8352026019","display_name":null,"funder_award_id":"R018634/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3083640775.pdf","grobid_xml":"https://content.openalex.org/works/W3083640775.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1720514416","https://openalex.org/W2054141820","https://openalex.org/W2100495367","https://openalex.org/W2101409192","https://openalex.org/W2140310134","https://openalex.org/W2253995343","https://openalex.org/W2605350416","https://openalex.org/W2725606191","https://openalex.org/W2739273093","https://openalex.org/W2762735242","https://openalex.org/W2767948492","https://openalex.org/W2883308936","https://openalex.org/W2885456372","https://openalex.org/W2886087818","https://openalex.org/W2889526258","https://openalex.org/W2932735187","https://openalex.org/W2948978827","https://openalex.org/W2963085847","https://openalex.org/W2965001869","https://openalex.org/W3104967912","https://openalex.org/W3122507327","https://openalex.org/W4206566734","https://openalex.org/W4288083766"],"related_works":["https://openalex.org/W3080500406","https://openalex.org/W2921491680","https://openalex.org/W2382684626","https://openalex.org/W2082325506","https://openalex.org/W3005155367","https://openalex.org/W2029507558","https://openalex.org/W2982493961","https://openalex.org/W2784194212","https://openalex.org/W2566662685","https://openalex.org/W2251863249"],"abstract_inverted_index":{"The":[0],"interactions":[1],"of":[2,45,77,170,188],"users":[3,36,190],"with":[4,226],"a":[5,103],"recommendation":[6,105,239],"system":[7],"are":[8,151],"in":[9,39],"general":[10],"sparse,":[11],"leading":[12],"to":[13,31,41,62,69,127,174,184],"the":[14,43,64,75,129,139,146,171,176,189,199,211,218,229],"well-known":[15],"cold-start":[16,130],"problem.":[17,131],"Side":[18],"information,":[19],"such":[20,241],"as":[21,60,157,168,194,242],"age,":[22],"occupation,":[23],"genre":[24],"and":[25,37,68,88,123,148,164,183,191,205,213,244],"category,":[26],"have":[27,52],"been":[28,54],"widely":[29],"used":[30],"learn":[32],"latent":[33,66,79,141,181,231],"representations":[34,80,182],"for":[35,56,114,153],"items":[38,192],"order":[40],"address":[42],"sparsity":[44],"users'":[46],"interactions.":[47],"Conditional":[48,109],"Variational":[49,110],"Autoencoders":[50],"(CVAEs)":[51],"recently":[53],"adapted":[55],"integrating":[57,154],"side":[58,90,125,155,166],"information":[59,91,126,156,167],"conditions":[61,137,144,227],"constrain":[63],"learned":[65,140,230],"factors":[67,232],"thereby":[70],"generate":[71],"personalised":[72,115,200],"recommendations.":[73],"However,":[74],"learning":[76],"effective":[78],"that":[81,217],"encapsulate":[82],"both":[83,121,210],"user":[84,122,163,223],"(e.g.":[85,92],"demographic":[86],"information)":[87],"item":[89,93,124,165,201],"categories)":[94],"is":[95],"still":[96],"challenging.":[97],"In":[98],"this":[99],"paper,":[100],"we":[101],"propose":[102],"new":[104],"model,":[106,113],"called":[107],"Hybrid":[108],"Autoencoder":[111],"(HCVAE)":[112],"top-n":[116,238],"recommendation,":[117],"which":[118],"effectively":[119],"integrates":[120],"tackle":[128],"Two":[132],"CVAE-based":[133],"methods":[134],"--":[135,150],"using":[136],"on":[138,145,209,222,228],"factors,":[142],"or":[143],"encoders":[147],"decoders":[149],"compared":[152],"conditions.":[158],"Our":[159],"HCVAE":[160,219],"model":[161,177,220],"leverages":[162],"part":[169],"optimisation":[172],"objective":[173],"help":[175],"construct":[178],"more":[179],"expressive":[180],"better":[185],"capture":[186],"attributes":[187],"(such":[193],"demographic,":[195],"category":[196,224],"preferences)":[197],"within":[198],"probability":[202],"distributions.":[203],"Thorough":[204],"extensive":[206],"experiments":[207],"conducted":[208],"MovieLens":[212],"Ta-feng":[214],"datasets":[215],"demonstrate":[216],"conditioned":[221],"preferences":[225],"can":[233],"significantly":[234],"outperform":[235],"common":[236],"existing":[237],"approaches":[240],"MF-based":[243],"VAE/CVAE-based":[245],"models.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-02T09:04:35.204637","created_date":"2025-10-10T00:00:00"}
