Recommender systems are one of the most successful applications of data
mining and machine learning technology in practice. Academic research in the
field is historically often based on the matrix completion problem formulation,
where for each user-item-pair only one interaction (e.g., a rating) is
considered. In many application domains, however, multiple user-item
interactions of different types can be recorded over time. And, a number of
recent works have shown that this information can be used to build richer
individual user models and to discover additional behavioral patterns that can
be leveraged in the recommendation process. In this work we review existing
works that consider information from such sequentially-ordered user- item
interaction logs in the recommendation process. Based on this review, we
propose a categorization of the corresponding recommendation tasks and goals,
summarize existing algorithmic solutions, discuss methodological approaches
when benchmarking what we call sequence-aware recommender systems, and outline
open challenges in the area.

本文提出了基于顺序的用户 - 项目交互日志信息来作出推荐的顺序感知推荐系统，并且探讨了现有的算法解决方案、方法论和该领域内的开放挑战。

序列感知推荐系统

Sequence-Aware Recommender Systems

Modeling the complex interactions between users and items as well as amongst
items themselves is at the core of designing successful recommender systems.
One classical setting is predicting users' personalized sequential behavior (or
`next-item' recommendation), where the challenges mainly lie in modeling
`third-order' interactions between a user, her previously visited item(s), and
the next item to consume. Existing methods typically decompose these
higher-order interactions into a combination of pairwise relationships, by way
of which user preferences (user-item interactions) and sequential patterns
(item-item interactions) are captured by separate components. In this paper, we
propose a unified method, TransRec, to model such third-order relationships for
large-scale sequential prediction. Methodologically, we embed items into a
`transition space' where users are modeled as translation vectors operating on
item sequences. Empirically, this approach outperforms the state-of-the-art on
a wide spectrum of real-world datasets. Data and code are available at
this https URL

本文提出了一种名为 TransRec 的方法，它可以将用户和项目之间以及项目之间的复杂交互建模，用于预测用户的个性化顺序行为，通过将项目嵌入到 “转换空间” 中，并使用用户来操作项目序列的翻译向量，以便捕捉第三阶交互关系。在广泛的真实世界数据集中，该方法表现优于现有方法。

基于翻译的推荐系统

Translation-based Recommendation

Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task.

我们提出了一种新的上下文递归神经网络，可以在输入和输出层中考虑相关信息，将上下文嵌入与项目嵌入相结合，并在模型动态性中将隐藏单元转换参数化为上下文信息的函数，从而在下一事件预测任务上显示出良好的提高效果。

使用递归神经网络进行推荐的上下文序列建模

Contextual Sequence Modeling for Recommendation with Recurrent Neural  Networks

Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
$\times 30$ times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.

本论文提出了一种基于神经网络的推荐框架，该框架可以包含用户 - 物品交互信息和多种内容信息，提出了三种新的采样策略来改善框架的训练效率和推荐性能，并对计算成本和收敛性进行了理论分析。