Big Data and Deep Learning Techniques Applied in Intelligent Recommender Systems

Abstract

With the rapid growth of big data and information all-round the internet, deep learning has become a solution to improve the quality of Recommender Systems (RS). Adapting to various kinds of data formats, modern deep learning methods can extract nontrivial hidden features from data in different fields, including deep semantic features from images and texts or relationships between users and items. However, some recently proposed algorithms have not yet been exploited by RS-related applications. In this paper, we will discuss several trendy deep learning networks with some of their notable variants in RS with respect to different kinds of data. In addition, we will provide an overview of approaches to choosing deep learning algorithms for specific recommendation tasks. Secondly, we will provide some possible research directions related to deep learning applications in RS.