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- W2899597932 abstract "Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Matrix Factorization (MF) is one of the most successful methods in recommendation. However, the rating matrix utilized by the MF-based models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem lies how to extract representative features from the noisy side information. In this paper, we propose Stacked Discriminative Denoising Auto-encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods." @default.
- W2899597932 created "2018-11-16" @default.
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- W2899597932 date "2018-01-01" @default.
- W2899597932 modified "2023-10-16" @default.
- W2899597932 title "Stacked Discriminative Denoising Auto-encoder Based Recommender System" @default.
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- W2899597932 doi "https://doi.org/10.1007/978-3-030-02698-1_24" @default.
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