Paper Abstract and Keywords |
Presentation |
2017-06-02 14:20
A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering Mohammed Hassan, Mohamed Hamada (Univ. of Aizu) SC2017-4 |
Abstract |
(in Japanese) |
(See Japanese page) |
(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) |
(in English) |
Recommender systems / Artificial Neural Networks / Aggregation function / Multi-criteria recommendation / Collaborative filtering / Prediction Accuracy / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 75, SC2017-4, pp. 17-20, June 2017. |
Paper # |
SC2017-4 |
Date of Issue |
2017-05-26 (SC) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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SC2017-4 |
Conference Information |
Committee |
SC |
Conference Date |
2017-06-02 - 2017-06-03 |
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 |
Paper Information |
Registration To |
SC |
Conference Code |
2017-06-SC |
Language |
English |
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) |
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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 |
Keyword(7) |
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Keyword(8) |
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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) |
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Speaker |
Author-1 |
Date Time |
2017-06-02 14:20:00 |
Presentation Time |
25 minutes |
Registration for |
SC |
Paper # |
SC2017-4 |
Volume (vol) |
vol.117 |
Number (no) |
no.75 |
Page |
pp.17-20 |
#Pages |
4 |
Date of Issue |
2017-05-26 (SC) |
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