Content-Based Personalized Recommender System Using Entity Embeddings
This addresses the need for better personalized recommendations in movie platforms, but it appears incremental as it builds on existing content-based methods with embeddings.
The paper tackled the problem of providing personalized movie recommendations by using a content-based approach with learned entity embeddings, resulting in improved recommendations based on user preferences for features like genre and keyword tags.
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.