WEKO3
-
RootNode
アイテム
Extended Bayesian Model for Multi-criteria Recommender System
https://ipsj.ixsq.nii.ac.jp/records/87802
https://ipsj.ixsq.nii.ac.jp/records/87802ab554645-e442-4c45-a72f-3942b852f79a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-IFAT13109006.pdf (244.2 kB)
|
Copyright (c) 2013 by the Information Processing Society of Japan
|
|
| オープンアクセス | ||
| Item type | SIG Technical Reports(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2013-01-04 | |||||||
| タイトル | ||||||||
| タイトル | Extended Bayesian Model for Multi-criteria Recommender System | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Extended Bayesian Model for Multi-criteria Recommender System | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
| 資源タイプ | technical report | |||||||
| 著者所属 | ||||||||
| The Graduated University for Advanced Study | ||||||||
| 著者所属 | ||||||||
| National Institute of Informatics／The Graduated University for Advanced Study | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| The Graduated University for Advanced Study | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| National Institute of Informatics / The Graduated University for Advanced Study | ||||||||
| 著者名 |
Pannawit, Samatthiyadikun
Atsuhiro, Takasu
× Pannawit, Samatthiyadikun Atsuhiro, Takasu
|
|||||||
| 著者名(英) |
Pannawit, Samatthiyadikun
Atsuhiro, Takasu
× Pannawit, Samatthiyadikun Atsuhiro, Takasu
|
|||||||
| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We have proposed multi-criteria (MC) recommender system by using a latent probabilistic model. In this model, users and items are mapped into small number of groups, and preference is represented based on the group instead of indivisual user. In other words, features of users and items are represented by probability distributions over latent topics. When predicting rating scores, we need to aggregate features into predicted rating score. This paper compares two ways to aggregate features for predicting rating score of unrated items in MC recommendation. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We have proposed multi-criteria (MC) recommender system by using a latent probabilistic model. In this model, users and items are mapped into small number of groups, and preference is represented based on the group instead of indivisual user. In other words, features of users and items are represented by probability distributions over latent topics. When predicting rating scores, we need to aggregate features into predicted rating score. This paper compares two ways to aggregate features for predicting rating score of unrated items in MC recommendation. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AN10114171 | |||||||
| 書誌情報 |
研究報告情報基礎とアクセス技術（IFAT） 巻 2013-IFAT-109, 号 6, p. 1-4, 発行日 2013-01-04 |
|||||||
| Notice | ||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
