Optimizing Recommendation Systems : A Hybrid Approach for Improved Accuracy

This recommender system model aims to develop a hybrid recommender system in Python to tackle the "cold start problem" prevalent in traditional recommender systems. Beginning with the aspiration to enhance the root mean squared error (RMSE) of existing recommendation algorithms, the project embarks on a comprehensive journey of data exploration, wrangling, and visualization. The dataset is scrutinized for missing or erroneous entries, necessitating advanced techniques such as value imputations with KNN for data restoration. Delving deeper, the project explores the creation of engineered features to enrich the training dataset. Leveraging a diverse array of machine learning algorithms, including content-based and collaborative filtering approaches, multiple models are constructed and evaluated for their efficacy in recommending items to users. Through iterative refinement and re-training on the augmented dataset, the project iterates towards the development of an improved hybrid recommender system. Ultimately, the system is assessed for its predictive accuracy, thereby enriching their online experiences.