User Cold Start Problem in Recommendation Systems: A Systematic Review

Abstract

The recommendation system makes recommendations based on the preferences of the users. These user preferences usually come from the user’s basic information, item rating, historical data, and so on. The “user cold start problem” happens when a new user cannot be appropriately suggested due to a lack of more detailed preference information. In many instances, the user cold start problem hinders the use of the recommendation system. Many researchers are currently trying to discover a solution to the user cold start problem. Unfortunately, there are two drawbacks in the current systematic reviews of how to deal with the user cold start problem. First, systematic reviews on how to deal with the user cold start problem are scarce or outdated. Second, existing reviews lack the distinction between the user cold start problem and the item cold start problem. Nevertheless, the solutions to the two problems differ. To address these problems, our study thorough review of all literature published by researchers from January 2016 to April 2023 about 8 years. Firstly, this study analyzes the literatures on approaches that addressed the user cold start problem during the past eight years and divides them into two categories: data-driven technology and approach-driven technology, and then describes and classifies each type of technology in detail. Secondly, this study also analyzes the main evaluation criteria currently used in these methods to provide a reference for researchers in related fields. Finally, this paper also points out the future research direction of this field.

Keywords

• Recommendation systems
• user cold start
• systematic review