Adaptive spectral graph wavelets for collaborative filtering
Osama Alshareet A. Ben Hamza
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.