Attribute-based Neural Collaborative Filtering

The core task of recommendation systems is to capture user preferences for items. Dot product operations are usually used to mine user preferences for items. However, the dot product can only capture the low order linear relationships between users and items. In addition, to alleviate the data sparsity problem, current methods mainly introduce auxiliary information, such as user/item attribute information. This attribute information is often treated equivalently. In fact, the importance of this information has different effects on the recommendation results. Therefore, in this paper, we propose a novel Attribute-based Neural Collaborative Filtering (ANCF) method to solve the above problems. Specifically, we use the attention mechanism to distinguish the importance of attribute information and integrate it into the corresponding user and item feature representations to obtain a complete feature representation of users and items. To further capture the high-order interactive relationship between users and items, we use a multi-layer perceptron in ANCF to fully learn the high-order nonlinear relationship between users and items. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed ANCF framework.

Automated summary

The core task of recommendation systems is to capture user preferences for items. In this paper, a novel Attribute-based Neural Collaborative Filtering (ANCF) method is proposed to solve the data sparsity problem and capture the high-order interactive relationship between users and items. The ANCF method distinguishes the importance of attribute information using an attention mechanism and utilizes a multi-layer perceptron to learn the high-order nonlinear relationship between users and items effectively.