Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

Abstract

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.

6 pages, 1 figure, 1 table, "Published with International Journal of Computer Applications (IJCA)"

References in corpus (5)

• Graph Convolutional Matrix Completion
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation
• Collaborative Filtering with Recurrent Neural Networks
• Recurrent Latent Variable Networks for Session-Based Recommendation
• Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation