At the age of big data, recommender systems have shown remarkable success as
a key means of information filtering in our daily life. Recent years have
witnessed the technical development of recommender systems, from perception
learning to cognition reasoning which intuitively build the task of
recommendation as the procedure of logical reasoning and have achieve
significant improvement. However, the logical statement in reasoning implicitly
admits irrelevance of ordering, even does not consider time information which
plays an important role in many recommendation tasks. Furthermore,
recommendation model incorporated with temporal context would tend to be
self-attentive, i.e., automatically focus more (less) on the relevance
(irrelevance), respectively.
To address these issues, in this paper, we propose a Time-aware
Self-Attention with Neural Collaborative Reasoning (TiSANCR) based
recommendation model, which integrates temporal patterns and self-attention
mechanism into reasoning-based recommendation. Specially, temporal patterns
represented by relative time, provide context and auxiliary information to
characterize the user's preference in recommendation, while self-attention is
leveraged to distill informative patterns and suppress irrelevances. Therefore,
the fusion of self-attentive temporal information provides deeper
representation of user's preference. Extensive experiments on benchmark
datasets demonstrate that the proposed TiSANCR achieves significant improvement
and consistently outperforms the state-of-the-art recommendation methods.

本文提出了一个基于时间感知的自我注意力协同推荐模型，将时间模式和自我注意力机制融入基于推理的推荐，提供更深层的用户偏好表示，并在基准数据集上进行了广泛的实验，表明该模型在推荐方面达到了显著的改进，并始终优于现有方法。

基于时间感知的自我注意力与逻辑推理相结合的推荐系统

Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems

Existing Collaborative Filtering (CF) methods are mostly designed based on
the idea of matching, i.e., by learning user and item embeddings from data
using shallow or deep models, they try to capture the associative relevance
patterns in data, so that a user embedding can be matched with relevant item
embeddings using designed or learned similarity functions. However, as a
cognition rather than a perception intelligent task, recommendation requires
not only the ability of pattern recognition and matching from data, but also
the ability of cognitive reasoning in data. In this paper, we propose to
advance Collaborative Filtering (CF) to Collaborative Reasoning (CR), which
means that each user knows part of the reasoning space, and they collaborate
for reasoning in the space to estimate preferences for each other. Technically,
we propose a Neural Collaborative Reasoning (NCR) framework to bridge learning
and reasoning. Specifically, we integrate the power of representation learning
and logical reasoning, where representations capture similarity patterns in
data from perceptual perspectives, and logic facilitates cognitive reasoning
for informed decision making. An important challenge, however, is to bridge
differentiable neural networks and symbolic reasoning in a shared architecture
for optimization and inference. To solve the problem, we propose a modularized
reasoning architecture, which learns logical operations such as AND ($\wedge$),
OR ($\vee$) and NOT ($\neg$) as neural modules for implication reasoning
($\rightarrow$). In this way, logical expressions can be equivalently organized
as neural networks, so that logical reasoning and prediction can be conducted
in a continuous space. Experiments on real-world datasets verified the
advantages of our framework compared with both shallow, deep and reasoning
models.

本文提出了一种新的协作推荐系统框架，即协作推理 (CR)，将学习和推理融为一体。该框架通过一种神经模块化的形式，将符号推理操作嵌入到基于神经网络的模型中，将逻辑表达式等价地组织为神经网络，从而实现了在连续空间中进行逻辑推理和预测。实验证明，与现有的浅层、深层和推理模型相比，本文提出的框架具有更好的推荐性能。