Application of Recommendation Systems Based on Deep Learning

Abstract

With the rapid development of mobile Internet and self-media, it is becoming more and more convenient for people to obtain information, and the problem of information overload has increasingly affected people’s sense of use. The emergence of recommendation systems can help solve the problem of information overload, but in the big data environment, the traditional recommendation system technology can no longer meet the needs of recommending more personalized, more real-time, and more accurate information to users. In recent years, deep learning has made breakthroughs in the fields of natural language understanding, speech recognition, and image processing. At the same time, deep learning has been integrated into the recommendation system, and satisfactory results have also been achieved. However, how to integrate massive multi-source heterogeneous data and build more accurate user and item models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences between recommendation systems based on deep learning and traditional recommendation systems.