This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems.
Intro
- We conduct a comprehensive review for recommendation models based on deep learning techniques and propose a classification scheme to position and organize the current work;
- we provide an overview and summary for the state of the art;
- we discuss the challenges and open issues, and identify the new trends and future directions in this research field to share the vision and expand the horizons of deep learning-based recommender systems research.
overview of recommender systems and deep Learning
- Introduction to the basic terminology and concepts regarding recommender systems and deep learning techniques.
- The reasons and motivations of introducing deep neural networks into recommender systems.
Recommender
- CF
- CB
- Hrbrid
Deep learning techniques
In this subsection, we clarify a diverse array of architectural paradigms that are closely related to this survey.
- MLP(The Multilayer Perceptron): a feed-forward neural network with multiple (one or more) hidden layers between the input and output layers.
- AE(An Autoencoder):an unsupervised model attempting to reconstruct its input data in the output layer
- CNN:a special kind of feedforward neural network with convolution layers and pooling operations. It can capture the global and local features and significantly enhances efficiency and accuracy. It performs well in processing data with grid-like topology.
- RNN: is suitable for modelling sequential data. Unlike the feedforward neural network, there are loops and memories in RNN to remember former computations. Variants such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are often deployed in practice to overcome the vanishing gradient problem.
- RBM：a two-layer neural network consisting of a visible layer and a hidden layer.