{"created":"2021-03-01T06:01:16.040070+00:00","id":8956,"links":{},"metadata":{"_buckets":{"deposit":"7c2f667f-0ec0-4062-ae93-05ab01d70907"},"_deposit":{"id":"8956","owners":[],"pid":{"revision_id":0,"type":"depid","value":"8956"},"status":"published"},"_oai":{"id":"oai:kitami-it.repo.nii.ac.jp:00008956","sets":["1:86"]},"author_link":["273","7687"],"item_1646810750418":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_3_alternative_title_198":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Reinforcement Learning using Fully Connected Neural Networks in Recommender System","subitem_alternative_title_language":"en"}]},"item_3_biblio_info_186":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"48","bibliographicPageStart":"41","bibliographicVolumeNumber":"21","bibliographic_titles":[{"bibliographic_title":"バイオメディカル・ファジィ・システム学会誌 = Journal of Biomedical Fuzzy Systems Association"}]}]},"item_3_description_184":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"従来研究では，推薦システムを表現するための確率モデルとしてマルコフ決定過程が採用されている．他方，多くの分野において，ニューラルネットワークを用いた強化学習方法が提案されている．しかし，推薦システムにおけるニューラルネットワークを用いた強化学習方法は提案されていない．そこで，本研究では，マルコフ決定過程の真のパラメータが未知の仮定のもとで推薦システムにおける全結合ニューラルネットワークを用いた強化学習方法を提案する．提案方法では顧客の性質を表現するために顧客の履歴情報を利用する．シミュレーションによって提案方法の有効性を示す．シミュレーション結果では，提案方法の出力が最適解と一致した．","subitem_description_type":"Abstract"},{"subitem_description":"[ENG]\nMarkov decision processes are applied to recommender system in previous research. Reinforcement learning methods using neural networks have been proposed in many fields. But a reinforcement learning method using neural networks has not been proposed in recommender system. In this research we propose a reinforcement learning method using fully connected neural networks in recommender system under the condition that the true parameters of Markov decision processes are unknown. The proposed method uses historical data of customers to represent customers' properties. The effectiveness of the proposed method is shown by some simulations. The output of the proposed method is equal to the optimal solution in the simulation result.","subitem_description_type":"Abstract"}]},"item_3_publisher_212":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"バイオメディカル・ファジィ・システム学会"}]},"item_3_relation_208":{"attribute_name":"論文ID（NAID）","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"40021956537","subitem_relation_type_select":"NAID"}}]},"item_3_rights_192":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright(c)2019 Biomedical Fuzzy Systems Association"}]},"item_3_select_195":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_3_source_id_187":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1345-1537","subitem_source_identifier_type":"PISSN"}]},"item_3_source_id_189":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1145146X","subitem_source_identifier_type":"NCID"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"前田, 康成","creatorNameLang":"ja"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"MAEDA, Yasunari","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-01-21"}],"displaytype":"detail","filename":"バイオメディカル・ファジィ・システム学会誌, 21(1), p41-48.pdf","filesize":[{"value":"1.3 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"バイオメディカル・ファジィ・システム学会誌, 21(1), p41-48","url":"https://kitami-it.repo.nii.ac.jp/record/8956/files/バイオメディカル・ファジィ・システム学会誌, 21(1), p41-48.pdf"},"version_id":"3c8356f9-2181-45ef-be42-b14fae5cbb18"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"推薦システム","subitem_subject_scheme":"Other"},{"subitem_subject":"マルコフ決定過程","subitem_subject_scheme":"Other"},{"subitem_subject":"強化学習","subitem_subject_scheme":"Other"},{"subitem_subject":"全結合ニューラルネットワーク","subitem_subject_scheme":"Other"},{"subitem_subject":"recommender system","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Markov decision processes","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"reinforcement learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"fully connected neural networks","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"推薦システムにおける全結合ニューラルネットワークを用いた強化学習","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"推薦システムにおける全結合ニューラルネットワークを用いた強化学習","subitem_title_language":"ja"},{"subitem_title":"Reinforcement Learning using Fully Connected Neural Networks in Recommender System","subitem_title_language":"en"}]},"item_type_id":"3","owner":"1","path":["86"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-01-21"},"publish_date":"2021-01-21","publish_status":"0","recid":"8956","relation_version_is_last":true,"title":["推薦システムにおける全結合ニューラルネットワークを用いた強化学習"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-12-13T02:22:36.021289+00:00"}