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A survey of autoencoder-based recommender systems

机译：基于自动编码器的推荐系统调查

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摘要

In the past decade, recommender systems have been widely used to provide users with personalized products and services. However, most traditional recommender systems are still facing a challenge in dealing with the huge volume, complexity, and dynamics of information. To tackle this challenge, many studies have been conducted to improve recommender system by integrating deep learning techniques. As an unsupervised deep learning method, autoencoder has been widely used for its excellent performance in data dimensionality reduction, feature extraction, and data reconstruction. Meanwhile, recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks. Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users' demands and characteristics of items. This paper reviews the recent researches on autoencoder-based recommender systems. The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper. At last, some potential research directions of autoencoder-based recommender systems are discussed.
机译：在过去的十年中，推荐系统已被广泛用于为用户提供个性化的产品和服务。但是，大多数传统推荐系统在处理海量信息，复杂性和动态信息方面仍面临挑战。为了应对这一挑战，已经进行了许多研究，以通过整合深度学习技术来改进推荐系统。作为一种无监督的深度学习方法，自动编码器因其在数据降维，特征提取和数据重构方面的出色性能而被广泛使用。同时，最近的研究表明自动编码器在信息检索和推荐任务中具有很高的效率。由于自动编码器可以更好地理解用户的需求和项目特征，因此在推荐系统上应用自动编码器可以提高建议的质量。本文综述了基于自动编码器的推荐系统的最新研究。本文介绍了基于自动编码器的推荐系统与传统推荐系统之间的差异。最后，讨论了基于自动编码器的推荐系统的一些潜在研究方向。

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