Abstract
Nowadays, smart devices are revolutionizing mobile computing systems, and recommender system are a major driving force. There are a wide variety of recommender systems, but the most popular is collaborative filtering where recommendation is given based on user preference. But recently, deep learning-based models like RBM and autoencoder are extensively being used in recommender system mainly because of their ability to model complex nonlinear data with activation functions, and moreover, making a deep learning-based model is comparatively much easier as compared to other models as they are extremely flexible. Using deep autoencoder as a model in recommendation is very new and is gaining popularity nowadays, and so we propose a deep autoencoder-based hybrid recommender model using collaborative filtering. We will demonstrate that (a) this model performs better than shallow autoencoders and (b) adding contextual information improves the model performance.
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Chowdhury, A.R., Pal, A. (2022). Hybrid Context-Aware Recommendation System Using Deep Autoencoder. In: Ranganathan, G., Fernando, X., Shi, F., El Allioui, Y. (eds) Soft Computing for Security Applications . Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_11
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DOI: https://doi.org/10.1007/978-981-16-5301-8_11
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