Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

TL;DR

This paper introduces BiGeaR, a novel framework for learning binarized graph representations in recommender systems, which enhances efficiency and performance by multi-faceted quantization reinforcement, achieving significant speed, memory savings, and near full-precision accuracy.

Contribution

BiGeaR employs multi-stage quantization reinforcement to preserve information in binarized embeddings, improving recommendation accuracy and efficiency over existing methods.

Findings

Achieves 22%-40% performance improvement over state-of-the-art quantization methods.

Recovers 95%-102% of full-precision performance.

Provides over 8x reduction in time and space complexity.

Abstract

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently shows the promising potentiality in optimizing both memory and computation overheads. However, existing work merely focuses on numerical quantization whilst ignoring the concomitant information loss issue, which, consequently, leads to conspicuous performance degradation. In this paper, we propose a novel quantization framework to learn Binarized Graph Representations for Top-K Recommendation (BiGeaR). BiGeaR introduces multi-faceted quantization reinforcement at the pre-, mid-, and post-stage of binarized representation learning, which substantially retains the representation informativeness against embedding binarization. In addition to saving the memory footprint, BiGeaR further develops solid online inference acceleration with bitwise operations, providing alternative flexibility for the realistic deployment. The empirical results over five large real-world benchmarks show that BiGeaR achieves about 22%~40% performance improvement over the state-of-the-art quantization-based recommender system, and recovers about 95%~102% of the performance capability of the best full-precision counterpart with over 8x time and space reduction.