A Survey of Session-Based Recommender Systems
Ankur Kumar Saxena · 2023
Session-based recommender systems have gained significant attention in recent years due to their ability to capture user preferences and provide personalized recommendations based on sequential interactions. These systems consider user sessions as a unit of analysis, taking into account the temporal dynamics and context of user behavior. This paper presents an overview of session-based recommender systems, discussing their key components and methodologies. The focus is on capturing the short-term interests and preferences of users by analyzing their recent session history. Various approaches, including collaborative filtering, recurrent neural networks, and hybrid models, are explored to model the session-context and make accurate recommendations. Additionally, the paper examines the challenges and opportunities associated with session-based recommender systems. These challenges include data sparsity, cold start problem, session representation, and scalability. Solutions and techniques to address these challenges are discussed, such as session encoding methods, session-based evaluation metrics, and data augmentation techniques. The evaluation of session-based recommender systems is also addressed, highlighting the metrics used to assess the effectiveness of recommendations, including precision, recall, and diversity. Furthermore, real-world applications and case studies of session-based recommender systems across various domains, such as e-commerce, online streaming, and news recommendation, are presented to showcase their practicality and impact. An experiment is also conducted to do a comparative analyses of many well-known techniques that are in use for making recommendations in sessions.