Abstract
We propose a recommendation system method which is based on NMF (Nonnegative Matrix Factorization) in collaborative filtering to enhance the rating predictions. The proposed method conduct selective imputations that fuses the factored original rating matrix and the factored imputed rating matrix into one system. The outputs of the factorized matrices provide four different ways to calculate the predicted ratings which are called sub-predicted ratings. Our proposed method is capable of predicting the rating by utilizing either the imputed users, or imputed items, or both in order to limit the errors that may be introduced from the imputed ratings. We proposed five strategies to calculate the final predicted rating from the sub-predicted ratings. The prediction results of rating values that are not close to the average of the rating values could be enhanced by utilizing the proposed method. Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach improves the predicted rating especially with Max of value category strategy.
Original language | English |
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Title of host publication | Proceedings of 3rd International Conference on Information System and Data Mining, ICISDM 2019 |
Pages | 119-128 |
Number of pages | 10 |
ISBN (Electronic) | 9781450366359 |
DOIs | |
State | Published - Apr 6 2019 |
Event | 3rd International Conference on Information System and Data Mining, ICISDM 2019 - Houston, United States Duration: Apr 6 2019 → Apr 8 2019 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 3rd International Conference on Information System and Data Mining, ICISDM 2019 |
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Country/Territory | United States |
City | Houston |
Period | 4/6/19 → 4/8/19 |
Bibliographical note
Publisher Copyright:© 2019 Association for Computing Machinery.
Keywords
- Collaborative filtering
- Imputation
- Nonnegative matrix factorization
- Recommendation system
- Trust matrix
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications