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Prior-based bayesian pairwise ranking for one-class collaborative filtering
https://tokushima-u.repo.nii.ac.jp/records/2008964
https://tokushima-u.repo.nii.ac.jp/records/20089645f7d0ac9-a559-4ffe-836c-c5b18dc1f3cd
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
neucom_440_365.pdf (600 KB)
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| Item type | 文献 / Documents(1) | |||||||||||||
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| 公開日 | 2021-06-04 | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版社版DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1016/j.neucom.2021.01.117 | |||||||||||||
| 言語 | ja | |||||||||||||
| 関連名称 | 10.1016/j.neucom.2021.01.117 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Prior-based bayesian pairwise ranking for one-class collaborative filtering | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Zhang, Qian
× Zhang, Qian
× 任, 福継
WEKO
401
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | In many real-world applications, only user-item interactions (one-class feedback) can be observed. The recommendation methods have been studied for personalized ranking with one-class feedback in recent years. Pairwise ranking methods have been widely used for dealing with the one-class problem with the assumption that users prefer their observed items over unobserved items. However, existing some items that users have not seen yet. It is unsuitable for treating all unobserved items of the user as negative feedback. In this paper, we propose a Prior-based Bayesian Pairwise Ranking (PBPR) model, which relaxes the simple pairwise preference assumption in previous works by further considering the pairwise preference between two unobserved items. Moreover, we calculate users' potential preference scores on unobserved items, i.e., prior information, based on historical interactions. The prior information can be used to measure the fine-grained preference difference between any two unobserved items of each user. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed recommendation method. | |||||||||||||
| 言語 | en | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | One-class collaborative filtering | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Prior information | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Pairwise ranking | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Recommendation method | |||||||||||||
| 書誌情報 |
en : Neurocomputing 巻 440, p. 365-374, 発行日 2021-02-18 |
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| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 09252312 | |||||||||||||
| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AA10827402 | |||||||||||||
| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AA11540468 | |||||||||||||
| 出版者 | ||||||||||||||
| 出版者 | Elsevier | |||||||||||||
| 言語 | en | |||||||||||||
| 権利情報 | ||||||||||||||
| 言語 | en | |||||||||||||
| 権利情報 | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||
| EID | ||||||||||||||
| 識別子 | 374057 | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
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| 言語 | eng | |||||||||||||
