论文标题
基于内容的个性化推荐系统使用实体嵌入
Content-Based Personalized Recommender System Using Entity Embeddings
论文作者
论文摘要
推荐系统是一类机器学习算法，可根据用户与类似项目的交互或基于项目内容的用户交互向用户提供相关建议。在要保留项目内容的设置中，基于内容的方法将是有益的。本文旨在通过学习的嵌入来强调基于内容的方法的优势，并利用这些优势，以根据用户偏好为各种电影功能（例如类型和关键字标签）提供更好和个性化的电影建议。
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.
