Social Network Recommendation Systems: A Review on Cold-Start Problems

The cold start problem significantly hampers the ability of social network recommendation systems to provide effective suggestions for new users, items, or communities due to a lack of initial data. This paper delves into various strategies to mitigate this challenge, highlighting collaborative filtering, which leverages existing user relationships to make recommendations; content-based methods, recommending trending content to new-comers; and hybrid approaches that blend these methods for improved accuracy. In addition, it examines advanced machine learning techniques like zero-shot learning, transfer learning, and active learning, which predict user preferences or use data from other domains to improve recommendations. The objective is to explore how these methodologies can improve the experience of onboarding on social networks, ensuring personalized and relevant suggestions from the start despite the initial absence of data.