Abstract
Recently, recommender systems are widely used on various platforms in real world to provide personalized recommendations. However, sparsity is a tough problem in a Collaborate Filtering (CF) recommender system as it always leads to the over-fitting problem. This paper proposes a Model-based Collaborate Filtering Algorithm Based on Stacked AutoEncoder (MCFSAE) to overcome the sparsity problem. In the MCFSAE model, we first convert the rating matrix into a high-dimensional classification dataset with a size equal to the number of ratings. As the number of ratings is usually large scale, the classification performance can be guaranteed. Since the obtained classification dataset is high dimensional, we then utilize Stacked AutoEncoder, which is a good nonlinear feature reduction model, to obtain a high-level low-dimensional feature presentation. Finally, a softmax classification model is used to predict the unknown ratings based on the high-level features. Extensive experiments on EachMovie and MovieLens datasets are conducted to compare the proposed MCFSAE model with other SOTA CF models. Experimental results show that MCFSAE performs better than other CF models, especially when the rating matrix is sparse.
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Acknowledgements
This work is jointly sponsored by National Natural Science Foundation of China (No. 61402246), Natural Science Foundation of Shandong Province (No. ZR2019MF014 and No. ZR2019PEE022), and China Textile Industry Federation Science and Technology Guidance Project (No. 2018078).
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Yu, M., Quan, T., Peng, Q. et al. A model-based collaborate filtering algorithm based on stacked AutoEncoder. Neural Comput & Applic 34, 2503–2511 (2022). https://doi.org/10.1007/s00521-021-05933-8
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DOI: https://doi.org/10.1007/s00521-021-05933-8