{"created":"2025-01-19T01:40:58.426206+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00238007","sets":["581:11492:11501"]},"path":["11501"],"owner":"44499","recid":"238007","title":["ユーザの特性を考慮した変分オートエンコーダを用いたアイテム推薦"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-08-15"},"_buckets":{"deposit":"2acdda5c-c6af-4dd0-9368-e5c310cf1e37"},"_deposit":{"id":"238007","pid":{"type":"depid","value":"238007","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ユーザの特性を考慮した変分オートエンコーダを用いたアイテム推薦","author_link":["651705","651706"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ユーザの特性を考慮した変分オートエンコーダを用いたアイテム推薦"},{"subitem_title":"Consideration of User Traits for Item Recommendation by Variational Autoencoder","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文] 推薦モデル，ユーザの特性，OCEAN（BigFive）特性，条件付き変分オートエンコーダ","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-08-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"デンソーアイティーラボラトリ"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Denso IT Laboratory","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/238007/files/IPSJ-JNL6508010.pdf","label":"IPSJ-JNL6508010.pdf"},"date":[{"dateType":"Available","dateValue":"2026-08-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6508010.pdf","filesize":[{"value":"704.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"3df05c54-c61e-4320-b68f-94a3ac486ea6","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"太刀岡, 勇気"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuuki, Tachioka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層学習に基づく推薦システムは，ビッグデータを処理するのに有効なことから，近年注目されている．とりわけ変分オートエンコーダ（VAE）に基づく推薦手法は，データがスパースな推薦タスクに有効である．しかしながら，ユーザの特性が推薦されたアイテムの嗜好に影響するため，VAEに基づく推薦手法の性能を向上させるには，ユーザの特性を注意深く考慮する必要がある．本報では，VAEをユーザの特性ラベルで条件付けることで，隠れ変数の生成モデルの分布を切り替える方法を提案する．音楽推薦タスクの実験により，ツイート履歴より得られるユーザの特性ラベルを用いることで推薦性能が向上することと，分布がユーザの特性に依存して変化することを示した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Deep learning-based recommendation algorithms have recently attracted attention due to their effectiveness at processing big data. Recommendation methods based on the variational autoencoder (VAE) are particularly promising thanks to their advantage with the data sparsity problem in recommendation tasks. However, because user traits affect the preference of recommended items, to improve the performance of VAE-based recommendation methods, it is necessary to carefully consider user traits. In this paper, we propose a method that conditions the VAE with user trait labels for switching the distributions of a generative model of latent variables. Experiments on a music recommendation task demonstrate that utilizing user trait labels estimated from tweet history leads to an improved recommendation performance and that the distribution can be changed depending on the individual traits of users.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1240","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1233","bibliographicIssueDates":{"bibliographicIssueDate":"2024-08-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00237887","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":238007,"updated":"2025-01-19T08:39:17.610404+00:00","links":{}}