| 講演名 | 2017-06-02 A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering Mohammed Hassan(会津大), Mohamed Hamada(会津大), |
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| PDFダウンロードページ |  PDFダウンロードページへ |
| 抄録(和) | Recommender systems (RSs) are intelligent decision-making tools that exploit users? preferences and suggest items that might be interesting to them. Traditionally, RSs use single ratings to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various items? attributes for improving the prediction accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed in employing artificial neural networks (ANNs) to determine the predictive performance of the systems based on aggregation function approach. The empirical results of the proposed techniques are compared with that of the traditional single rating-based techniques |
| 抄録(英) | Recommender systems (RSs) are intelligent decision-making tools that exploit users? preferences and suggest items that might be interesting to them. Traditionally, RSs use single ratings to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various items? attributes for improving the prediction accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed in employing artificial neural networks (ANNs) to determine the predictive performance of the systems based on aggregation function approach. The empirical results of the proposed techniques are compared with that of the traditional single rating-based techniques |
| キーワード(和) | Recommender systems / Artificial Neural Networks / Aggregation function / Multi-criteria recommendation / Collaborative filtering / Prediction Accuracy |
| キーワード(英) | Recommender systems / Artificial Neural Networks / Aggregation function / Multi-criteria recommendation / Collaborative filtering / Prediction Accuracy |
| 資料番号 | SC2017-4 |
| 発行日 | 2017-05-26 (SC) |
| 研究会情報 | |
| 研究会 | SC |
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| 開催期間 | 2017/6/2(から2日開催) |
| 開催地（和） | 会津大学(UBIC 3D) |
| 開催地（英） | University of Aizu(UBIC 3D) |
| テーマ（和） | 「IoT, ビッグデータ解析，知能システムのためのサービスコンピューティング」および一般 |
| テーマ（英） | Service Computing including IoT, Big Data Analytics, Intelligent Communication System, and Other Issues |
| 委員長氏名（和） | Incheon Paik(会津大) |
| 委員長氏名（英） | Incheon Paik(Univ. of Aizu) |
| 副委員長氏名（和） | 中村 匡秀(神戸大) |
| 副委員長氏名（英） | Masahide Nakamura(Kobe Univ.) |
| 幹事氏名（和） | 田仲 正弘(NICT) / 細野 繁(NEC) |
| 幹事氏名（英） | Masahiro Tanaka(NICT) / Shigeru Hosono(NEC) |
| 幹事補佐氏名（和） | |
| 幹事補佐氏名（英） | |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Service Computing |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Recommender systems / Recommender systems |
| キーワード(2)（和/英） | Artificial Neural Networks / Artificial Neural Networks |
| キーワード(3)（和/英） | Aggregation function / Aggregation function |
| キーワード(4)（和/英） | Multi-criteria recommendation / Multi-criteria recommendation |
| キーワード(5)（和/英） | Collaborative filtering / Collaborative filtering |
| キーワード(6)（和/英） | Prediction Accuracy / Prediction Accuracy |
| 第 1 著者 氏名（和/英） | Mohammed Hassan / Mohammed Hassan |
| 第 1 著者 所属（和/英） | The University of Aizu(略称：会津大) The University of Aizu(略称：Univ. of Aizu) |
| 第 2 著者 氏名（和/英） | Mohamed Hamada / Mohamed Hamada |
| 第 2 著者 所属（和/英） | The University of Aizu(略称：会津大) The University of Aizu(略称：Univ. of Aizu) |
| 発表年月日 | 2017-06-02 |
| 資料番号 | SC2017-4 |
| 巻番号（vol） | vol.117 |
| 号番号（no） | SC-75 |
| ページ範囲 | pp.17-20(SC), |
| ページ数 | 4 |
| 発行日 | 2017-05-26 (SC) |