Source-linked AI summary
Sequential Recommender Systems: Challenges, Progress and Prospects
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Orgun
TL;DR
Sequential recommender systems must model complex, sequentially dependent user-item interactions rather than isolated events. This paper systematically reviews their characteristics, challenges, research progress, and future directions.
Problem
Sequential recommender systems involve complex interaction sequences whose richer structure makes them more challenging to analyze than simpler sequences.
Method
The paper systematically categorizes SRS challenges from a data-driven perspective, research progress from a technical perspective, and prospects for future work.
Results
The review provides an overview of SRS characteristics, key challenges, recent research progress, and future directions.
Takeaways & Limitations
The summary is intended to support the recommender-systems research community’s understanding of challenges, progress, and prospects in SRSs.
Takeaways & Limitations
The paper notes that such a model is easily affected.
Abstract
from arXiv · showhide
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.
1 Introduction
Sequential recommender systems model sequentially dependent user–item interactions together with changing user preferences, item popularity, and interaction contexts. This review organizes their data-driven challenges, summarizes technical progress, and discusses future research directions.
- Motivation: Why Sequential Recommender Systems?: SRSs model user–item interactions as sequentially dependent behaviors rather than isolated events, using prior interactions to predict near-future item interactions.Real-world shopping actions often occur successively, such as booking travel components before visiting an attraction.
- Motivation: Why Sequential Recommender Systems?: User preferences and item popularity change over time, making dynamic modeling important for precise user and item profiling.The passage contrasts declining iPhone popularity with growing interest in Huawei or Samsung phones.
- Motivation: Why Sequential Recommender Systems?: SRS learning is challenging because each sequence element can be a complex user–action–item triple rather than a simple atomic element.A ranking function may output candidate-item utility through conditional probability or interaction scores.
- Contributions: The review systematically analyzes data-driven challenges, categorizes state-of-the-art research technically, and discusses prospects for the SRS community.Future directions include context-aware and social-aware SRSs, which existing systems often ignore.
- 2 Data Characteristics and Challenges: Key challenges arise from diverse interaction-data structures, including long sequences, flexible order, noise, heterogeneous relations, and hierarchical dependencies.These characteristics require different solutions and are organized into five challenge categories.