Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems

TL;DR

This paper reviews how deep learning techniques are applied to collaborative filtering recommender systems, highlighting current trends, gaps, and future research directions in this rapidly evolving field.

Contribution

It provides a comprehensive review of deep learning-based collaborative filtering methods, identifying key challenges and areas that need further exploration.

Findings

Deep learning enhances recommendation accuracy and scalability.

Cold start and data sparsity remain significant challenges.

Many areas in deep learning-based CF are still underexplored.

Abstract

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.