Recommender systems play important roles in various applications such as
e-commerce, social media, etc. Conventional recommendation methods usually
model the collaborative signals within the tabular representation space.
Despite the personalization modeling and the efficiency, the latent semantic
dependencies are omitted. Methods that introduce semantics into recommendation
then emerge, injecting knowledge from the semantic representation space where
the general language understanding are compressed. However, existing
semantic-enhanced recommendation methods focus on aligning the two spaces,
during which the representations of the two spaces tend to get close while the
unique patterns are discarded and not well explored. In this paper, we propose
DisCo to Disentangle the unique patterns from the two representation spaces and
Collaborate the two spaces for recommendation enhancement, where both the
specificity and the consistency of the two spaces are captured. Concretely, we
propose 1) a dual-side attentive network to capture the intra-domain patterns
and the inter-domain patterns, 2) a sufficiency constraint to preserve the
task-relevant information of each representation space and filter out the
noise, and 3) a disentanglement constraint to avoid the model from discarding
the unique information. These modules strike a balance between disentanglement
and collaboration of the two representation spaces to produce informative
pattern vectors, which could serve as extra features and be appended to
arbitrary recommendation backbones for enhancement. Experiment results validate
the superiority of our method against different models and the compatibility of
DisCo over different backbones. Various ablation studies and efficiency
analysis are also conducted to justify each model component.

我们提出了一种名为 DisCo 的方法，以增强推荐系统，其中捕获了两个表示空间的特异性和一致性，然后将这些特征作为额外特征附加到任意的推荐主干中，实验证实了我们方法的优越性和兼容性。

DisCo: 面向推荐的表格和语义空间之间的和谐解耦和协作

DisCo: Towards Harmonious Disentanglement and Collaboration between  Tabular and Semantic Space for Recommendation

We show that a distributed network of robots or other devices which make
measurements of each other can collaborate to globally localise via efficient
ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian
Belief Propagation on the fundamental non-linear factor graph describing the
probabilistic structure of all of the observations robots make internally or of
each other, and is flexible for any type of robot, motion or sensor. We define
a simple and efficient communication protocol which can be implemented by the
publishing and reading of web pages or other asynchronous communication
technologies. We show in simulations with up to 1000 robots interacting in
arbitrary patterns that our solution convergently achieves global accuracy as
accurate as a centralised non-linear factor graph solver while operating with
high distributed efficiency of computation and communication. Via the use of
robust factors in GBP, our method is tolerant to a high percentage of faults in
sensor measurements or dropped communication packets.

通过机器人之间的 peer-to-peer 通信，在使用高斯信念传播在非线性因子图上对机器人网络观测的概率结构进行计算的基础上，我们实现了一个灵活的 Robot Web 解决方案，可以解决千个机器人相互交互的问题，即使测量传感器或通信数据包出错，该方法也具有容错性。