Title: Network Shape Automata: a brain network inspired collaborative filter for link prediction in bipartite complex networks and recommendation systems
Primary area: learning on graphs and other geometries & topologies
Keywords: collaborative filtering, recommendation systems, bipartite network, link prediction

Abstract: In recommendation systems, representing user-item interactions as a bipartite network is a fundamental approach that provides a structured way to model relationships between users and items, allowing for efficient predictions via network science. Collaborative filtering is one of the most widely used and actively researched techniques for recommendation systems, its rationale is to predict user preferences based on shared patterns in user interactions, and vice versa. Memory-based collaborative filtering relies on directly analyzing user-item interactions to provide recommendations using similarity measures, and differs from model-based collaborative filtering which builds a predictive model using machine learning techniques such as neural networks. With the rise of machine learning, memory-based collaborative filtering has often been overshadowed by model-based approaches. However, the recent success of SSCF, a newly proposed memory-based method, has renewed interest in the potential of memory-based approaches. In this paper, we propose Network Shape Automata (NSA), a memory-based collaborative filtering method grounded in the connectivity shape of the bipartite network topology. NSA leverages the Cannistraci-Hebb theory proposed in network science to define brain-inspired network automata, using this paradigm as the foundation for its similarity measure. We evaluate NSA against a range of advanced collaborative filtering methods, both memory-based and model-based, across 13 middle-scale bipartite network datasets spanning complex systems domains such as social networks and biological networks and 3 classical large-scale recommentation datasets including Gowalla, Yelp2018, Amazon-book. Results show that NSA consistently achieves strong performance across diverse datasets and evaluation metrics, ranking most often first on average. Notably, NSA demonstrates strong robustness to network sparsity, while preserving the simplicity, interpretability, and training-free nature of memory-based methods. As a pioneering effort to bridge link prediction and recommendation tasks, NSA not only highlights the untapped potential of memory-based collaborative filtering but also demonstrates the effectiveness of the Cannistraci-Hebb theory in modeling network evolution within recommendation systems.