Abstract
In the field of Data science and online world, Recommendation Systems (RS) play an important role among the various e-commerce applications. Data sparsity often leads to the problem of precise recommendations in RS as there will be either less number of users or ratings. Collaborative filtering (CF) is one of the key techniques that are used for the RS with the pre-requisite of the adequate information of the users and items. Deep Learning (DL) models haved paved the way for analysis and prediction of sequential textual information in various applications. Hence, CF combined with DL approaches are also being explored to solve the problem of data sparsity in RS with various challenges of analysis and prediction of the sequential information. This paper considers the problem of data sparsity with a novel neural CF based DeepLSGR model to provide better recommendations. It is a bi-directional model composed of stacked hidden layers with Long-short term memory (LSTM) and Gated recurrent unit (GRU) and provide recommendations based on the prediction of rating using the textual reviews from the users. It provided an accuracy of 97%, recall of 61% and RMSE of 0.87 for the experiments conducted on the Amazon Fine Food Reviews and OpinRank datasets. The results of the comparison with the existing works evidently demonstrate that the DeepLSGR provides improved recommendations.
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Hiriyannaiah, S., G M, S. & Srinivasa, K.G. DeepLSGR: Neural collaborative filtering for recommendation systems in smart community. Multimed Tools Appl 82, 8709–8728 (2023). https://doi.org/10.1007/s11042-021-11551-2
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DOI: https://doi.org/10.1007/s11042-021-11551-2