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.
