Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning
Zhuo Liu, Yunan Yang, Zhenyu Pan, Anshujit Sharma, Amit Hasan, Caiwen Ding, Ang Li, Michael Huang, Tong Geng
Abstract
Due to the Ising model’s strong expressivity and Ising machines’ unique computational power, it is highly desired if Ising-based learning can be used in real-world applications. Unfortunately, the challenges in learning the Ising model and gaps between the practical accuracy of Ising machines and the theoretical accuracy of the Ising model impede the realization of Ising machines’ potential. Hence, we propose an Ising Machine Learning framework, Ising-CF, for collaborative filtering, a widely-used recommendation method. Specifically, Ising-CF uses Linear Neural Networks with Besag’s pseudo-likelihood and voltage polarization for fast, accurate Ising model learning and an Ising-specific logarithmic quantization for ns-level Ising machine inference with near-theoretical accuracy, 7.3% over SOTA.