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

TL;DR

This paper provides a comprehensive overview of sequential and session-based recommender systems, addressing inconsistencies, summarizing key challenges, approaches, applications, and future research directions to advance the field.

Contribution

It offers a unified framework and systematic analysis of SRSs and SBRSs, consolidating diverse descriptions, assumptions, and applications into a coherent overview.

Findings

Identifies key data characteristics and challenges in SR/SBR

Summarizes state-of-the-art approaches and applications

Outlines future research directions in the field

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.