| Presentation | 2017-06-02 A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering Mohammed Hassan, Mohamed Hamada, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | 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 |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Recommender systems / Artificial Neural Networks / Aggregation function / Multi-criteria recommendation / Collaborative filtering / Prediction Accuracy |
| Paper # | SC2017-4 |
| Date of Issue | 2017-05-26 (SC) |
| Conference Information | |
| Committee | SC |
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| Conference Date | 2017/6/2(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | University of Aizu(UBIC 3D) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Service Computing including IoT, Big Data Analytics, Intelligent Communication System, and Other Issues |
| Chair | Incheon Paik(Univ. of Aizu) |
| Vice Chair | Masahide Nakamura(Kobe Univ.) |
| Secretary | Masahide Nakamura(NICT) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Service Computing |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering |
| Sub Title (in English) | |
| Keyword(1) | Recommender systems |
| Keyword(2) | Artificial Neural Networks |
| Keyword(3) | Aggregation function |
| Keyword(4) | Multi-criteria recommendation |
| Keyword(5) | Collaborative filtering |
| Keyword(6) | Prediction Accuracy |
| 1st Author's Name | Mohammed Hassan |
| 1st Author's Affiliation | The University of Aizu(Univ. of Aizu) |
| 2nd Author's Name | Mohamed Hamada |
| 2nd Author's Affiliation | The University of Aizu(Univ. of Aizu) |
| Date | 2017-06-02 |
| Paper # | SC2017-4 |
| Volume (vol) | vol.117 |
| Number (no) | SC-75 |
| Page | pp.pp.17-20(SC), |
| #Pages | 4 |
| Date of Issue | 2017-05-26 (SC) |