Collaborative filtering is a popular approach in recommender systems, whose
objective is to provide personalized item suggestions to potential users based
on their purchase or browsing history. However, personalized recommendations
require considerable amount of behavioral data on users, which is usually
unavailable for new users, giving rise to the cold-start problem. To help
alleviate this challenging problem, we introduce a spectral graph wavelet
collaborative filtering framework for implicit feedback data, where users,
items and their interactions are represented as a bipartite graph.
Specifically, we first propose an adaptive transfer function by leveraging a
power transform with the goal of stabilizing the variance of graph frequencies
in the spectral domain. Then, we design a deep recommendation model for
efficient learning of low-dimensional embeddings of users and items using
spectral graph wavelets in an end-to-end fashion. In addition to capturing the
graph's local and global structures, our approach yields localization of graph
signals in both spatial and spectral domains, and hence not only learns
discriminative representations of users and items, but also promotes the
recommendation quality. The effectiveness of our proposed model is demonstrated
through extensive experiments on real-world benchmark datasets, achieving
better recommendation performance compared with strong baseline methods.

我们提出了一种基于谱图小波的协同过滤框架，用于隐式反馈数据的个性化推荐，通过将用户、物品及其交互表示为一个二分图，利用自适应转换函数和深度推荐模型，来学习用户和物品的低维表示，同时促进推荐质量。实验证明，相比基准方法，我们的模型在真实世界的基准数据集上取得了更好的推荐效果。