Abstract:
Recommendation Systems have been built over the years using various machine learning (ML), deep learning (DL), and
natural language processing (NLP) techniques. In this research, we introduce a novel hybrid recommendation system that incorporates
sentiment analysis (using NLTK), item-based filtering algorithms, and user-based recommendations. The system intends to outperform
previous systems in terms of suggestion quality and robustness by exploiting ensemble models. The study makes use of a proprietary
dataset compiled from various sources, including Amazon, Tmdb, and Google reviews. The Synthetic Minority Oversampling
Technique (SMOTE) is used to alleviate class imbalance. Textual inputs are subsequently converted into numerical representations for
modeling using feature extraction techniques. The ensemble model incorporates supervised machine learning methods such as logistic
regression (LR), Naive Bayes (NB), Gini decision trees (DT), random forest (RF), and XGBoost. The system provides personalized
recommendation outputs by analyzing the input of each model, revolutionizing the recommendation environment. Our hybrid system
attains a commendable accuracy score of 96% attained by the XGBoost algorithm. In this study, we propose a novel hybrid
recommendation system based on sentiment analysis and item-based filtering that leverages ensemble techniques going beyond existing
approaches. Furthermore, our findings emphasize the significance of benchmark datasets and evaluation measures, particularly in deep
learning-based RS, giving useful insights for both researchers and practitioners. Overall, our study adds a new viewpoint to the literature
by focusing solely on the fast-growing domain of deep learning-based recommendation systems, providing a nuanced knowledge of
the advances, problems, and prospects in this crucial field of research.
