Deep Learning-Based Recommendation Systems: Review and Critical Analysis

Recommendation systems (RSs) belong to a category of information filtering systems designed to predict the “ranking” or “preference” that users will give to a particular item. RSs are automated instruments and strategies that assist and increase the decision-making process by aggregating the views of individuals and guiding them to suitable recipients. RSs are extensively utilized in various domains, including e-commerce, social networking sites, and entertainment, and impact everyone’s everyday life. The systems are designed to assist the user by proposing the items that are appropriate for him or her without requiring them to undergo the lengthy, time-consuming, and complex process of selecting from a wide selection of items that can number in the thousands or millions. The major aim of making recommendations based on the user’s interests is to minimize human work. Models and algorithms are expected to catch different user preferences and mostly identify non-dependencies between them and the multitude of items to provide personalization. In addition, this problem is compounded by real data criteria and ambitious real-time requirements. Many difficulties arise when developing and operating RSs. Therefore, it is compulsory to address them and design a system in which they become mitigated or tolerable. Sparsity, Cold Start, and Scalability are a few challenges when a user develops a recommendation system. The pervasive use of deep learning has demonstrated its power in solving complicated tasks more efficiently than conventional techniques. This paper seeks to stimulate advancements in RSs by providing a thorough summary of recent research on recommendation systems using deep learning. The surveyed articles are categorized using a taxonomy of recommendation systems that are offered. Based on the analysis of the evaluated works and the stated potential solutions, open problems are highlighted.