Building Recommender Systems with Machine Learning and Data Mining Techniques Building Recommender Systems with Machine Learning and Data Mining Techniques

Abstract

The current study presents a unique use of machine learning algorithms for developing a recommendation system. Recommender systems are often employed in a wide range of industries, including e-commerce, entertainment, and search engines. Recommender systems are algorithms that utilize user preferences and behavior to recommend relevant objects, such as movies, books, goods, or songs. This article examines the many characteristics and features of different methodologies used in recommendation systems, with an emphasis on filtering and prioritizing important information to serve as a compass for searches. When recommender engines properly recommend individualized content or items, they provide businesses with a competitive advantage over rivals and generate considerable income. This study investigates content-based filtering, which suggests things with comparable attributes to those that a user previously liked. It also investigates hybrid filtering, which combines content-based and collaborative filtering techniques (using user-item interaction data) to solve issues such as the cold start problem (little user data) and data sparsity (few user-item interactions). The installed recommender systems that use content-based and hybrid filtering approaches produce promising individualized suggestions. Content-based filtering is particularly effective at proposing comparable goods, but hybrid filtering provides a more diversified and accurate suggestion pool by including user preferences. Content-based filtering has limits owing to data sparsity, which hybrid filtering addresses. This article discovered that the suggested technique uses content-based filtering when applied to small to medium-sized datasets. However, hybrid filtering is used when the dataset is vast and sparse.