Recommendation System in Industrial data analysis using Machine Learning Techniques

The interactions of users are seamlessly integrated into the product life cycle in the Industry structure of the future. Recommender systems function as a means of filtering information by providing users with recommendations according to their interests in items, music, friends, and topics. Numerous research endeavours have aimed to enhance recommendation accuracy through various techniques like matrix factorization, trust-based approaches, hybrids, and both machine and deep learning methods. However, few studies have explored the utilization of textual opinions for recommendations. Existing research primarily relies on numerical ratings, which may not accurately represent user behavior. This study delves into analyzing the sentiments expressed in textual opinions to gain a deeper understanding of user behavior. Furthermore, textual opinions are processed through the use of methods from Natural Language Processing such as stop-word removal, lemmatization, stemming, and Part-of-Speech (POS) tagging. The aim of a recommendation system is to anticipate user preferences and understand their thought patterns. By considering user preferences and demands, the system delivers tailored information. A deeper examination of the data is necessary to enhance recommendation quality.