Efficient Neural Interaction Function Search for Collaborative Filtering
This work addresses the challenge of inconsistent performance in collaborative filtering across various applications, offering a more adaptable solution for recommendation systems.
The paper tackles the problem of designing effective interaction functions for collaborative filtering by proposing a search for simple neural interaction functions (SIF) using an automated machine learning approach, resulting in improved prediction performance over state-of-the-art methods and efficient discovery of distinct functions for different datasets.
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, which has been successfully used in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Thus, other operations (such as plus and concatenation), which may potentially offer better performance, have been proposed. Nevertheless, it is still hard for existing IFCs to have consistently good performance across different application scenarios. Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF. By examining and generalizing existing CF approaches, an expressive SIF search space is designed and represented as a structured multi-layer perceptron. We propose an one-shot search algorithm that simultaneously updates both the architecture and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, can obtain much better prediction performance than state-of-the-art CF approaches, and can discover distinct IFCs for different data sets and tasks