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.

翻译：推荐系统在电子商务、社交媒体等众多应用中发挥着重要作用。传统的推荐方法通常在表格表示空间内对协同信号进行建模。尽管实现了个性化建模和高效率，但潜在的语义依赖关系却被忽略了。随后出现了将语义引入推荐的方法，这些方法从压缩了通用语言理解的语义表示空间中注入知识。然而，现有的语义增强推荐方法侧重于对齐这两个空间，在此过程中，两个空间的表示趋于接近，而独特的模式却被丢弃且未能得到充分探索。本文提出DisCo方法，旨在从两个表示空间中解耦出独特模式，并协同这两个空间以增强推荐性能，从而同时捕捉两个空间的特异性和一致性。具体而言，我们提出了：1）一个双端注意力网络，用于捕获域内模式和跨域模式；2）一个充分性约束，以保留每个表示空间中与任务相关的信息并过滤噪声；3）一个解耦约束，以防止模型丢弃独特信息。这些模块在解耦与协同两个表示空间之间取得了平衡，以生成信息丰富的模式向量，这些向量可作为额外特征附加到任意的推荐主干模型中以实现性能增强。实验结果验证了我们的方法相对于不同模型的优越性，以及DisCo在不同主干模型上的兼容性。我们还进行了多种消融研究和效率分析，以验证每个模型组件的有效性。