| Research Title |
Improving Dynamic Recommender System Based on Item Clustering for Preference Drifts |
| Date of Distribution |
13 July 2018 |
| Conference |
| Title of the Conference |
The 15th International Joint Conference on Computer Science and Software Engineering (JCSSE2018) |
| Organiser |
Faculty of ICT, Mahidol University |
| Conference Place |
Faculty of ICT, Mahidol University |
| Province/State |
Nakhon Pathom, THAILAND |
| Conference Date |
11 July 2018 |
| To |
13 July 2018 |
| Proceeding Paper |
| Volume |
2018 |
| Issue |
25 |
| Page |
418-423 |
| Editors/edition/publisher |
IEEE |
| Abstract |
The recommender system is an efficient tool for online application, which exploits historical user rating on item to make recommendations on items to users. This paper aims to enhance dynamic recommender systems under volatile user preference drifts. It proposed an algorithm to solve sparse data by using Gaussian mixture model to fill in data matrix for sparsity reduction and improve more completely ratings prediction. Subsequently, it utilizes item clustering and linear regression technique to predict the future interests of users in category based and additionally uses the nearest neighbor method to prevent over-fitting. The experimental results show that the proposed approach provides the better performance on rating prediction when compared with the state-of-the-art dynamic recommendation algorithms. |
| Author |
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| Peer Review Status |
มีผู้ประเมินอิสระ |
| Level of Conference |
นานาชาติ |
| Type of Proceeding |
Full paper |
| Type of Presentation |
Oral |
| Part of thesis |
true |
| Presentation awarding |
true |
| Award Title |
Best Paper Awards : Machine Learning |
| Type of award |
รางวัลด้านวิชาการ วิชาชีพ |
| Organiser |
JCSSE2018 |
| Date of awarding |
13 กรกฎาคม 2561 |
| Attach file |
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| Citation |
0
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Publication
Journal Publication
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