A Performant Predict Analytics Approach to Recommender Systems Using Deep Learning Methods

Abstract

Recently, Massive amounts of data have been generated as a result of the frequency at which the amount of information available digitally is advancing. To enable users to effectively utilise the huge amount of information, recommendation system has been implemented to effectively manipulate a large amount of data in other to communicate necessary output to the user. The reliability of the final recommendations is a common metric used to determine if a recommendation system is effective or not. The RMSE, MSE, and MAE were used as recommendation-based metrics. The recommender system's performance is typically measured using the metrics RMSE, MSE, and MAE. This statistic demonstrates how effectively the Recommender system performs. The performance of the recommendations improves with decreasing RMSE, MAE, and MSE. It offers an erroneous value that illustrates how far off from the real data our model was. It assesses how closely the projections supplied correlate to the quantities that were observed. The final result of evaluating RMSE, MAE, and MSE on 1M Movies Datasets. Taking the mean result of the output, MAE outperformed other matric because it has the lowest mean value of 0.5843. Also, evaluating results of algorithm SVD on the 100k movies dataset MAE outperformed other matric having the lowest output of 0.6593. Furthermore, evaluating the RMSE, MAE, and MSE of algorithm SVD on 5k movies data set, MAE still outperformed other matric having the lowest mean value of 2.8898. Ultimately, it was discovered that the movie data sets with the most customers and reviews performed better than the others with fewer datasets obtainable. Additionally, we suggest a deep learning approach for creating efficient and accurate deep learning collaborative filtering systems (DLCFS). The proposed method and the currently used methods were compared.