Content-Based Personalized Recommender System Using Entity Embeddings

Abstract

Recommender systems are a class of machine learning algorithms that provide relevant recommendations to a user based on the user's interaction with similar items or based on the content of the item. In settings where the content of the item is to be preserved, a content-based approach would be beneficial. This paper aims to highlight the advantages of the content-based approach through learned embeddings and leveraging these advantages to provide better and personalised movie recommendations based on user preferences to various movie features such as genre and keyword tags.