DeCS: A Deep Neural Network Framework for Cold Start Problem in Recommender Systems

With the exponential growth of e-commerce platforms, recommendation systems are widely used in predicting user interests, improving user experience, and increasing the number of sales. However, recommendation performance degrades for users who have very little interaction or new users who have never opted for the service. Consequently, the recommender systems cannot suggest items and services to these users due to the cold start issue. Naturally, a compelling demand for an efficient recommender system is essentially needed to guide users toward items of their interests. This paper proposes a deep neural network (DNN) framework that addresses the cold start problem in recommendation systems. The proposed framework named “DeCS” works primarily in stages that involve creating embeddings and vectors followed by training and prediction of three fundamental metrics-mean square error (MSE), mean absolute error (MAE), and root MSE (RMSE) by the framework. Several experiments evaluate the DeCS framework for different recommender metrics at various datasets. Predictions show that the proposed DeCS model achieves the MSE, RMSE, and MAE metrics in the range of 0.4338-1.2911, 0.6883-1.1362, and 0.4691-0.8745, respectively. Further, the result shows that the proposed approach improves these metrics by 15.81% compared to many state-of-the-art methods.