Learning Recommender Systems with Deep Structured Low Rank Matrix Approximation
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In this study we propose a Deep structured LOw Rank Matrix Approximation model (DLORMA) that incorporates additional stacked denoising autoencoders and local matrix approximations in a loosely coupled fashion. To the best of our knowledge, DLORMA is the first hybrid recommendation system that combines deep learning and low rank matrix approximation. Comprehend experiments based on three real datasets show improvements in prediction performance over other state-of-the-art recommendation systems.
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Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2020
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niknafskermani-learningrecommendersystemswithdeepstructured.pdf | 2023-05-05 | Public | Download |