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  1. 学術雑誌掲載済論文
  2. 和雑誌

推薦システムの新規顧客問題における半教師付き学習

https://kitami-it.repo.nii.ac.jp/records/8679
9d610400-aabc-4d06-8098-68cf44bdc7ae
名前 / ファイル ライセンス アクション
推薦システムの新規顧客問題における半教師付き学習.pdf 推薦システムの新規顧客問題における半教師付き学習 (3.0 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2018-12-11
タイトル
言語 ja
タイトル 推薦システムの新規顧客問題における半教師付き学習
タイトル
言語 en
タイトル Semi-supervised Learning for a New Customer Problem in Recommender System
言語
言語 jpn
キーワード
主題Scheme Other
主題 推薦システム
キーワード
主題Scheme Other
主題 マルコフ決定過程
キーワード
主題Scheme Other
主題 新規顧客問題
キーワード
主題Scheme Other
主題 半教師付き学習
キーワード
言語 en
主題Scheme Other
主題 recommender system
キーワード
言語 en
主題Scheme Other
主題 Markov decision processes
キーワード
言語 en
主題Scheme Other
主題 new customer problem
キーワード
言語 en
主題Scheme Other
主題 semi-supervised learning
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ journal article
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
その他のタイトル
その他のタイトル Semi-supervised Learning for a New Customer Problem in Recommender System
言語 en
著者 前田, 康成

× 前田, 康成

WEKO 273
KAKEN - 研究者検索 30422033

ja 前田, 康成

Search repository
山内, 翔

× 山内, 翔

WEKO 90064

ja 山内, 翔

Search repository
鈴木, 正清

× 鈴木, 正清

WEKO 89671
KAKEN - 研究者検索 60192621

ja 鈴木, 正清

Search repository
松嶋, 敏泰

× 松嶋, 敏泰

WEKO 90082

ja 松嶋, 敏泰

Search repository
MAEDA, Yasunari

× MAEDA, Yasunari

WEKO 7687

en MAEDA, Yasunari

Search repository
Yamauchi, Sho

× Yamauchi, Sho

WEKO 90067

en Yamauchi, Sho

Search repository
SUZUKI, Masakiyo

× SUZUKI, Masakiyo

WEKO 7691

en SUZUKI, Masakiyo

Search repository
Matsushima, Toshiyasu

× Matsushima, Toshiyasu

WEKO 9451

en Matsushima, Toshiyasu

Search repository
抄録
内容記述タイプ Abstract
内容記述 従来研究では,推薦システムの新規顧客問題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,マルコフ決定過程の真のパラメータが既知の仮定のもとで検討されている.本研究では,より現実に近い真のパラメータが未知の仮定のもとで推薦システムの新規顧客問題における半教師付き学習方法を提案する.学習データは完全データと不完全データによって構成される.提案方法ではEMアルゴリズム(expectation-maximization algorithm)を用いる.数例のシミュレーションによって提案方法の有効性を示す.
抄録
内容記述タイプ Abstract
内容記述 Markov decision processes(MDP) are applied to a new customer problem of recommender system in previous research. In the previous research the true parameters of MDP are known. In this research we propose a semi-supervised learning method for the new customer problem of recommender system under the condition that the true parameters of MDP are unknown. Learning data consist of complete data and incomplete data. In the proposed method EM(expectation-maximization) algorithm is used. The effectiveness of the proposed method is shown by some simulations.
書誌情報 バイオメディカル・ファジィ・システム学会誌
en : Journal of Biomedical Fuzzy Systems Association

巻 20, 号 1, p. 37-46, 発行日 2018-05
ISSN
収録物識別子タイプ PISSN
収録物識別子 2424-2578
権利
権利情報 c 2018 Biomedical Fuzzy Systems Association
出版者
出版者 バイオメディカル・ファジィ・システム学会
著者版フラグ
値 publisher
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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