Recommendation systems are highly employed information filtering systems that aim to suggest items to the user based on the user-s interest in the items. Personalized recommendations are designed to provide recommendations with customized preferences of the user based on the user-s historical data on the desired item. Personalized product recommendation suggests the product based on the customer purchase history and interest.
   Customized product recommendations are unable to process undefined reviews or text from the customers and unavailable information about the customers. Topic modeling is an unsupervised approach that recognizes and categorizes the data to discover latent semantic structures without any predefined training. Topic modeling in a recommendation system analyses the instructive information from customer reviews and has the ability to implicit the probability of the customer interest towards the product. Topic modeling-based personalized product recommendation suggests more relevant products to the customer and enables computationally efficient recommendations.