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Sequential Recommender Systems: Challenges, Progress and Prospects

Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Orgun

TL;DR

The characteristics of SRSs are presented, and the key challenges in this research area are summarized and categorized, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.

Abstract

The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.

Sequential Recommender Systems: Challenges, Progress and Prospects

TL;DR

The characteristics of SRSs are presented, and the key challenges in this research area are summarized and categorized, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.

Abstract

The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.

Paper Structure

This paper contains 13 sections, 1 equation, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Data Characteristics and Challenges
  3. Handling Long User-Item Interaction Sequences
  4. Handling User-Item Interaction Sequences with a Flexible Order
  5. Handling User-Item Interaction Sequences with Noise
  6. Handling User-Item Interaction Sequences with Heterogeneous Relations
  7. Handling User-Item Interaction Sequences with Hierarchical Structures
  8. Research Progress
  9. Traditional Sequence Models for SRSs
  10. Latent Representation Models for SRSs
  11. Deep Neural Network Models for SRSs
  12. Open Research Directions
  13. Conclusions

Figures (2)

  • Figure 1: Two examples of SRSs: (1) After Jimmy has booked a flight, a hotel and rented a car, what will be his next action? (2) After Tina has bought an iPhone, an iWatch and a pair of AirPods, what would she buy next?
  • Figure 2: A categorization of SRS approaches from the technical perspective