Content-based Recommender System with Python

What are recommender systems

• Recommender systems are methods that predict users’ interests and make meaningful recommendations to them for different items, such as songs to play on Spotify, movies to watch on Netflix, news to read about your favourite newspaper website or  products to purchase on Amazon.

Types of recommender systems

• Content-based recommenders rely on attributes of users and/or items, whereas collaborative filtering uses information on the interaction between users and items, expressed in the so-called user-item interaction matrix.

Types of recommenders -2

• Recommender systems are generally divided into 3 main approaches: content-based, collaborative filtering, and hybrid recommendation systems

Vectorization of data - TF-IDF approach

• TF-IDF of a word in a document which is part of a larger corpus of documents is a combination of two values. One is term frequency (TF), which measures how frequently the word occurs in the document.