Zhang, H., Ganchev, I., Nikolov, N.S. et al. (1 more author) (2021) UserReg : a simple but strong model for rating prediction. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 06-11 Jun 2021, Toronto, ON, Canada. IEEE (Institute of Electrical and Electronics Engineers) ISBN 9781728176062
Abstract
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate their performance in comparison to baselines or existing models in terms of accuracy improvements. However, others have pointed out that many newly proposed models are not as strong as expected and are outperformed by very simple baselines.This paper proposes a simple linear model based on Matrix Factorization (MF), called UserReg, which regularizes users’ latent representations with explicit feedback information for rating prediction. We compare the effectiveness of UserReg with three linear CF models that are widely-used as baselines, and with a set of recently proposed complex models that are based on deep learning or graph techniques. Experimental results show that UserReg achieves over-all better performance than the fine-tuned baselines considered and is highly competitive when compared with other recently proposed models. We conclude that UserReg can be used as a strong baseline for future CF research.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. |
Keywords: | Recommender Systems; Collaborative Filtering; Matrix Factorization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jun 2021 10:10 |
Last Modified: | 14 Jun 2021 10:10 |
Status: | Published |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Refereed: | Yes |
Identification Number: | 10.1109/icassp39728.2021.9413646 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175135 |