Abstract
Recommender systems plays a crucial role in machine learning algorithms that offer relevant suggestions to users. A recommender system is used to give preferences to users based on their past behaviors. Most of the previous works are designed using Collaborative filtering method,item-based collaborative filtering techniques. These models are less accurate, as we added optimization for the recommendation task. Another issue we are facing by these methods are missing values, prediction ratings, and top recommendations by using the above methods. To overcome these issues we introduced deep learning models like Restricted Boltzmann Machine for collaborative filtering. In this work, we proposed Deep Scalable and Distributed Restricted Boltzmann Machine, which can distinguish users past preferences in profile and make accurate predictions and text-based Top-N Recommendations. The experiments are conducted by using regulation parameter 0.1, learning rate 0.01. Experimental results and evaluation of the proposed model are done by the most widely used MovieLens-10M and Movie Lens- 20M datasets. The performance evaluation of the proposed model with RMSE (0.7721,0.7564),MAE (0.5928,0.5788), MSE (76.29,78.52) and Hit Ratio (0.6812, 0.6738) are observed and are competitive when compared with other existing works.
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Kumar, R.R.S.R., Apparao, G. & Anuradha, S. Deep scalable and distributed restricted boltzmann machine for recommendations. Int J Syst Assur Eng Manag 15, 161–173 (2024). https://doi.org/10.1007/s13198-022-01684-4
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DOI: https://doi.org/10.1007/s13198-022-01684-4