Recommendation Systems Features: A Comprehensive Review

The rise of Web 2.0 applications facilitated effortless content generation via social media platforms, collaborative tools, and interactive interfaces, significantly contributing to the vast amount of online data. However, this abundance of data overwhelms users, causing information overload and making it difficult to locate relevant content. Recommendation Systems (RS) address this issue by using methods that prioritize personalization and relevance, aiming to deliver content that aligns with users’ preferences. These systems analyze user preferences by leveraging diverse features and are supported by a wide array of models designed to enrich those features. To effectively understand and utilize these features and enrichment models, a comprehensive classification is essential. This paper presents an overview of the features used in recommendation systems and the methods employed to enhance them. Identifies and discusses key challenges related to features, providing valuable insights into potential future research directions.