Enhancing Hybrid Filtering for Evolving Recommender Systems: Dealing with Data Sparsity and Cold Start Problems

The recommendation system has become common due to the widespread use of the Internet and the availability of a wide range of products. It serves as a useful tool in assisting decision-making on online purchases. Challenges in recommender system such as difficulties in starting a system from scratch, unavailability of important data, excessive focus on specific areas, lack of up-to-date information, scarcity of data, and incorrect metadata. The traditional recommendation system relies on the collaborative filtering algorithm. The most effective suggestion techniques are Collaborative Filtering and Content Based Filtering. Despite their popularity, these filtering systems still suffer from several limitations, including the Cold Start Problem, Sparsity, and Scalability, all of which result in subpar suggestions and to provide a recommendation-anticipation system based on a hybrid approach in this article. The suggested solution combines content-based filtering with collaborative filtering. This research established a model for an online shopping recommendation system that takes into account both people and products by analyzing two types of conventional algorithms.