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.

本论文对顺序推荐系统进行了系统综述并总结了该领域的关键挑战，最近和典型发展以及未来的重要研究方向。

序列推荐系统：挑战、进展和前景

Sequential Recommender Systems: Challenges, Progress and Prospects

Recommender systems that can learn from cross-session data to dynamically
predict the next item a user will choose are crucial for online platforms.
However, existing approaches often use out-of-the-box sequence models which are
limited by speed and memory consumption, are often infeasible for production
environments, and usually do not incorporate cross-session information, which
is crucial for effective recommendations. Here we propose Hierarchical Temporal
Convolutional Networks (HierTCN), a hierarchical deep learning architecture
that makes dynamic recommendations based on users' sequential multi-session
interactions with items. HierTCN is designed for web-scale systems with
billions of items and hundreds of millions of users. It consists of two levels
of models: The high-level model uses Recurrent Neural Networks (RNN) to
aggregate users' evolving long-term interests across different sessions, while
the low-level model is implemented with Temporal Convolutional Networks (TCN),
utilizing both the long-term interests and the short-term interactions within
sessions to predict the next interaction. We conduct extensive experiments on a
public XING dataset and a large-scale Pinterest dataset that contains 6 million
users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than
RNN-based models and uses 90% less data memory compared to TCN-based models. We
further develop an effective data caching scheme and a queue-based mini-batch
generator, enabling our model to be trained within 24 hours on a single GPU.
Our model consistently outperforms state-of-the-art dynamic recommendation
methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.

本研究提出了一种使用 Hierarchical Temporal Convolutional Networks (HierTCN) 的推荐系统，以动态预测用户将选择的下一个物品，该模型由两个级别的模型组成，可用于拥有大量用户和物品的 Web 规模系统，并可以超越动态推荐方法的最新水平。

用于动态推荐系统的分层时间卷积网络

Hierarchical Temporal Convolutional Networks for Dynamic Recommender  Systems

Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.

该论文通过建立推荐系统与企业盈利的新型连接，针对如何制定动态推荐策略，从而实现最大化收益的问题，提出了一种考虑价格、评估、饱和效应和产品之间竞争等多种因素的新模型，并利用基于 matroid 理论的精确连接以及智能启发式算法，对该模型进行了求解和验证。