A Movie Recommender System with Collaborative and Content Filtering

 In the Internet age, we perceive the use of recommender systems all around us. The exponential growth of information from intelligent devices on the internet creates confusion for customers to pick a preferred product. Suggestions are a noble way to guide shoppers to discover fascinating products to impress customers. These recommender systems influence our browsing or watching or listening, searching patterns, and guess what customers might like in the future based on our patterns. For instance, a customer searching for baby products recommend diapers. Two significant categories of recommender systems exist, which are either collaborative or content filtering. The core of the recommender system resides in filtering similar users (or products). We address the introduction, existing works focusing on collaborative and content recommender filters, and their merits and demerits. Later, we classify types therein and thoroughly discuss similarity metrics used to filter neighborhood and evaluation measures used in the recommender system. 

Keywords: Filtering, Movie recommendation, Similarity metrics, Quality metrics.