A Progressive Approach for a Personalized Recommendation System Using Hybrid Deep Learning

A recommendation system is a type of statistical processing mechanism that anticipates and recommends goods, services, or content to users based on those users' preferences, interests, or activity history. The purpose of a recommendation system is to improve the customer experience and enable consumers in finding pertinent and worthwhile products or information. Our digital lives are now completely dependent on personalised recommendation algorithms, which assist users in finding information that suits their interests. In order to improve recommendation quality, this paper provides advanced deep learning techniques, personalised re-ranking model and hybrid recommendation approaches. In the initial stage, we retrieve context illustration using explicit, latent unstructured and latent structured data. Whereas a comprehensive study that categorises personalised recommendation models and investigates their advantages and disadvantages, measurement tools employed, and well-liked datasets. The following parameter are applied in our technique, analysing user behaviour, implementing fuzzy techniques on search data currently available and Applying context aware recommendation using deep learning techniques. Our end-user receives the best results produced after applying re-ranking model. The suggested model has 91% accuracy, 0.93 precision, 0.84 recall, and 0.87 F-Measure, according to our comparison of it with other models.