TL;DR
This paper introduces ChebyCF, a novel spectral graph filtering framework for collaborative filtering that leverages raw user interaction data and Chebyshev interpolation to overcome limitations of traditional graph neural networks, achieving state-of-the-art results.
Contribution
ChebyCF replaces embedding layers with spectral filtering on raw interaction data and uses Chebyshev interpolation for flexible non-linear filtering, improving recommendation performance.
Findings
Achieves state-of-the-art performance on multiple benchmarks.
Effectively leverages full spectrum of user interaction signals.
Provides reasonably fast inference compared to existing methods.
Abstract
Graph convolutional networks have recently gained prominence in collaborative filtering (CF) for recommendations. However, we identify potential bottlenecks in two foundational components. First, the embedding layer leads to a latent space with limited capacity, overlooking locally observed but potentially valuable preference patterns. Also, the widely-used neighborhood aggregation is limited in its ability to leverage diverse preference patterns in a fine-grained manner. Building on spectral graph theory, we reveal that these limitations stem from graph filtering with a cut-off in the frequency spectrum and a restricted linear form. To address these issues, we introduce ChebyCF, a CF framework based on graph spectral filtering. Instead of a learned embedding, it takes a user's raw interaction history to utilize the full spectrum of signals contained in it. Also, it adopts Chebyshev…
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