Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation
Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Computer Science and Information Technology, University of Malaya
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/72552/ http://psasir.upm.edu.my/id/eprint/72552/1/Embedded%20learning%20for%20leveraging%20multi-aspect%20in%20rating%20prediction%20of%20personalized%20recommendation.pdf |
| _version_ | 1848857146363478016 |
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| author | Khairudin, Nurkhairizan Mohd Sharef, Nurfadhlina Mohd Noah, Shahrul Azman Mustapha, Norwati |
| author_facet | Khairudin, Nurkhairizan Mohd Sharef, Nurfadhlina Mohd Noah, Shahrul Azman Mustapha, Norwati |
| author_sort | Khairudin, Nurkhairizan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand users' specific behaviours. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of the item. This situation happened due to the emphasis on aspects that may be different among users and will effect a user's final decisions. Therefore, in this paper, we proposed a model called Neural Network model for Multi-Aspect with Strong Correlation (NNMASC) that utilize the significance of aspect’s correlation to enhance the predictive accuracy of personalized recommendation. We integrate the user, item, aspects and overall ratings via embedding features by utilizing the available multi-aspect ratings from hotel reviews dataset. NNMASC adopts a feed-forward neural network with back propagation algorithm to make rating prediction. The experimental result using MAE shows that the proposed model has significantly outperformed the traditional models and the state-of-the-art approaches in terms of prediction accuracy. |
| first_indexed | 2025-11-15T11:52:55Z |
| format | Article |
| id | upm-72552 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:52:55Z |
| publishDate | 2018 |
| publisher | Faculty of Computer Science and Information Technology, University of Malaya |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-725522020-10-28T07:08:23Z http://psasir.upm.edu.my/id/eprint/72552/ Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation Khairudin, Nurkhairizan Mohd Sharef, Nurfadhlina Mohd Noah, Shahrul Azman Mustapha, Norwati Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand users' specific behaviours. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of the item. This situation happened due to the emphasis on aspects that may be different among users and will effect a user's final decisions. Therefore, in this paper, we proposed a model called Neural Network model for Multi-Aspect with Strong Correlation (NNMASC) that utilize the significance of aspect’s correlation to enhance the predictive accuracy of personalized recommendation. We integrate the user, item, aspects and overall ratings via embedding features by utilizing the available multi-aspect ratings from hotel reviews dataset. NNMASC adopts a feed-forward neural network with back propagation algorithm to make rating prediction. The experimental result using MAE shows that the proposed model has significantly outperformed the traditional models and the state-of-the-art approaches in terms of prediction accuracy. Faculty of Computer Science and Information Technology, University of Malaya 2018-12 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72552/1/Embedded%20learning%20for%20leveraging%20multi-aspect%20in%20rating%20prediction%20of%20personalized%20recommendation.pdf Khairudin, Nurkhairizan and Mohd Sharef, Nurfadhlina and Mohd Noah, Shahrul Azman and Mustapha, Norwati (2018) Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation. Malaysian Journal of Computer Science (spec.). 31 - 47. ISSN 0127-9084 https://ejournal.um.edu.my/index.php/MJCS/article/view/15486 |
| spellingShingle | Khairudin, Nurkhairizan Mohd Sharef, Nurfadhlina Mohd Noah, Shahrul Azman Mustapha, Norwati Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title | Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title_full | Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title_fullStr | Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title_full_unstemmed | Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title_short | Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| title_sort | embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation |
| url | http://psasir.upm.edu.my/id/eprint/72552/ http://psasir.upm.edu.my/id/eprint/72552/ http://psasir.upm.edu.my/id/eprint/72552/1/Embedded%20learning%20for%20leveraging%20multi-aspect%20in%20rating%20prediction%20of%20personalized%20recommendation.pdf |