ADVANCED RECOMMENDATION SYSTEMS THROUGH DEEP LEARNING
https://doi.org/10.1145/3386723.3387870
Mohmed Khoali
Laaziz Yassin
ofijan abuvisibility
…
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8 pages
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Abstract
Many companies have realized the importance of deep learning, which justifies their growing interest in the use of recommender systems to boost their sales. They aim to predict users' intents and recommend products likely to be of their interests. The purpose of this study is to provide a review of deep learning techniques for recommendation systems that have been used in research and industry.
Key takeaways
- [3] Identified the various benefits of developing a recommendation system using deep learning techniques.
- When two or more neural network are combined together for the recommendation system, it falls into the second category which is the deep hybrid models.
- We review these neural network extensions of collaborative filtering as a deep learning technique.
- The recommendation task was considered as sequential interactions between users and the recommender agent using deep reinforcement learning.
- Deep learning techniques have generally proved to be better at recommendation tasks as a nonlinear model than the linear models.
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Collaborative Deep Learning for Recommender Systems Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. Inspired by recent success demonstrated by deep learning models, we propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which tightly couples a Bayesian formulation of the stacked denoising autoencoder and probabilistic matrix factorization. Extensive experiments on real-world datasets show that CDL can significantly advance the state of the art.
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