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.

本文旨在提出统一框架和问题陈述，以填补序列推荐系统（SRS）和基于 session 的推荐系统（SBRS）的现有不一致性，并提供数据特征，难点，现有方法，实际应用和未来研究方向的全面系统演示，以促进这个领域的进一步研究。

序列 / 会话推荐：挑战、方法、应用和机遇

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

An effective online recommendation system should jointly capture users'
long-term and short-term preferences in both users' internal behaviors (from
the target recommendation task) and external behaviors (from other tasks).
However, it is extremely challenging to conduct fast adaptations to real-time
new trends while making full use of all historical behaviors in large-scale
systems, due to the real-world limitations in real-time training efficiency and
external behavior acquisition. To address these practical challenges, we
propose a novel Long Short-Term Temporal Meta-learning framework (LSTTM) for
online recommendation. It arranges user multi-source behaviors in a global
long-term graph and an internal short-term graph, and conducts different
GAT-based aggregators and training strategies to learn user short-term and
long-term preferences separately. To timely capture users' real-time interests,
we propose a temporal meta-learning method based on MAML under an asynchronous
optimization strategy for fast adaptation, which regards recommendations at
different time periods as different tasks. In experiments, LSTTM achieves
significant improvements on both offline and online evaluations. It has been
deployed on a widely-used online recommendation system named WeChat Top
Stories, affecting millions of users.

提出了一种基于 LSTTM 框架的在线推荐系统，它可以同时捕捉用户的长期和短期兴趣，采用不同的聚合方法和训练策略分别学习用户的短期和长期偏好，并引入了一种基于 MAML 的时间元学习方法以快速适应实时变化的用户兴趣。该框架在 WeChat Top Stories 推荐系统上的离线评估和在线评估中都取得了显著的改进。

在线推荐中的长短期时间元学习

Long Short-Term Temporal Meta-learning in Online Recommendation

Recommender systems (RSs) have been playing an increasingly important role
for informed consumption, services, and decision-making in the overloaded
information era and digitized economy. In recent years, session-based
recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different
from other RSs such as content-based RSs and collaborative filtering-based RSs
which usually model long-term yet static user preferences, SBRSs aim to capture
short-term but dynamic user preferences to provide more timely and accurate
recommendations sensitive to the evolution of their session contexts. Although
SBRSs have been intensively studied, neither unified problem statements for
SBRSs nor in-depth elaboration of SBRS characteristics and challenges are
available. It is also unclear to what extent SBRS challenges have been
addressed and what the overall research landscape of SBRSs is. This
comprehensive review of SBRSs addresses the above aspects by exploring in depth
the SBRS entities (e.g., sessions), behaviours (e.g., users' clicks on items)
and their properties (e.g., session length). We propose a general problem
statement of SBRSs, summarize the diversified data characteristics and
challenges of SBRSs, and define a taxonomy to categorize the representative
SBRS research. Finally, we discuss new research opportunities in this exciting
and vibrant area.

本文回顾了 session-based recommender systems 的现有研究，总结了数据特征和挑战，并提出了新的研究机会。