Revisiting Collaborative Filtering by Unleashing the Power of Similarity

While deep learning-based approaches dominate the research on collaborative filtering (CF) in recommender systems, recent studies have re-examined this paradigm and found that deep models do not consistently outperform traditional CF alternatives on standard evaluation metrics. Notably, similarity-based methods exhibit substantial advantages. Inspired by these observations, we demonstrate that the performance of a minimalist similarity-based model can match or exceed leading CF approaches, highlighting its significant potential. Despite this, the expressiveness of high-performing similarity-based models is fundamentally confined to pairwise interactions within immediate neighborhoods, overlooking the combinatorial preference patterns in high-order connections. To overcome these limitations and fully unleash the potential of similarity-based models, we propose a novel bidirectional extension framework that systematically generalizes the modeling of similarity to group-level and multi-hop perspectives. However, directly computing these generalized similarities is prohibitively expensive due to their combinatorial complexity. To address this, we develop a MinHash-based estimation algorithm, which ensures the feasibility of the computation and reduces the complexity from exponential to linear. Extensive experiments on three public datasets show that our framework achieves competitive or superior performance over strong baselines, yielding relative improvements ranging from 4% to 11% on key metrics, underscoring the enduring power of similarity-based methods in recommender systems.