Session based recommendation system using gradient descent temporal CNN for e-commerce application

In practice, the most important aspect has been to suggest items of interest to users based on their prior choices. In recent decades, recommendation systems have been highly successful in achieving this goal by using a variety of methods. Profile reliance is generally used as a critical element in traditional recommendation systems. Considering this impact is difficult to address, academics have been interested in analyzing user behavior in an ongoing session. Based on this insight, Researchers developed session-based recommendation to address this challenge of recommending products to users based on their ongoing interactions. Session-based Recommendation is a sequential method of determining user-item interactions that doesn't require access to the user's profile information or their entire history of preferences. Due to its capacity to analyze non-linear data, a novel gradient descent-based temporal convolution neural network is developed in this study to meet the session-based recommendation. Non-linear distortion, knowledge discovery, sequence mapping, and adaptability are the key benefits of adopting Deep Learning for recommendation systems. Utilizing the box-cox transformation paradigm, the primary data transformation stage is implemented to change the invalid data into valid information. Second, a novel gradient descent temporal convolution neural network (GDTCNN), created specifically for e-commerce applications, is proposed to carry out the session-based recommendations. When compared to the current methods, the suggested model has a better accuracy rating of 99.49, demonstrating its superiority.