Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this article, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.

中文翻译：

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推荐系统的分离项目表示

推荐系统中的项目表示有望揭示项目的属性。协作推荐方法通常将一个项目表示为一个单一的潜在向量。如今，电子商务平台提供了各种物品的属性信息（例如，类别、价格和服装款式）。近年来，利用此属性信息来更好地表示项目是很流行的。一些研究使用给定的属性信息作为辅助信息，将其与项目潜在向量连接以增强表示。然而，混合项目表示未能充分利用丰富的属性信息或在推荐系统中提供解释。为此，我们在本文中为推荐系统提出了一种细粒度的分离项目表示（DIR），其中项目表示为几个分离的属性向量，而不是单个潜在向量。通过这种方式，项目在属性级别表示，可以提供推荐项目的细粒度信息。我们引入了一种学习策略 LearnDIR，它可以为项目分配相应的属性向量。我们展示了如何将 DIR 应用于两个典型模型，即矩阵分解 (MF) 和递归神经网络 (RNN)。在两个真实世界数据集上的实验结果表明，在 DIR 框架下开发的模型是有效且高效的。即使使用更少的参数，所提出的模型也可以胜过最先进的方法，尤其是在冷启动情况下。此外，我们进行可视化以表明我们的命题可以为实际应用程序中的用户提供解释。