Content-Based Personalized Recommender System Using Entity Embeddings

Xavier Thomas

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arXiv · 2010.12798

Content-Based Personalized Recommender System Using Entity Embeddings

Xavier Thomas

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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.

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Xavier Thomas. 2020-10-24. Content-Based Personalized Recommender System Using Entity Embeddings. https://arxiv.org/abs/2010.12798

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