Content-Based Personalized Recommender System Using Entity Embeddings

TL;DR

This paper presents a content-based recommender system that uses entity embeddings to improve personalized movie recommendations by capturing user preferences for features like genre and keywords.

Contribution

It introduces a novel approach leveraging entity embeddings for content-based recommendations, enhancing personalization and recommendation quality.

Findings

Entity embeddings improve recommendation relevance

Personalization based on movie features is more effective

The approach outperforms traditional content-based methods

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.