Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

Abstract

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real-world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.

References in corpus (7)

• Session-based Recommendation with Graph Neural Networks
• Session-based Recommendations with Recurrent Neural Networks
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• Sequential Recommender Systems: Challenges, Progress and Prospects
• Empirical Analysis of Session-Based Recommendation Algorithms
• Deep Reinforcement Learning for List-wise Recommendations
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

Cited by in corpus (3)

• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
• TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
• A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives