A Semi-supervised Co-training Algorithm for Robust Recommendation

Wu Yue

doi:10.1145/3773988

A Semi-supervised Co-training Algorithm for Robust Recommendation

2026-01-01

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DOI：10.1145/3773988

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Recommendation algorithms have been extensively applied in multiple areas. However, user-level uncertainty in implicit feedback introduce false-positive signals, misaligning interactions with true preferences and introducing noise. This makes it challenging for traditional recommendation methods to achieve both robust and accurate performance. In this manuscript, we propose a semi-supervised co-training algorithm for denoising learning in the recommender system, which incorporates both supervised learning and semisupervised learning in a cooperative way. We first adopt a Gaussian Mixture Model as a confidence estimation module to partition the implicit feedback into two disjoint datasets: a reliable dataset and an unreliable dataset, based on the intrinsic features of the feedback. To achieve doubly robust learning, we design an iterative cross co-training module that consists of two key components: a two-stage reliable learning process and a cross-sample transmission mechanism. The two-stage reliable learning process enables semi-supervised training on both the reliable and unreliable datasets. Meanwhile, the cross-sample transmission mechanism iteratively transfers hard yet clean samples between dual networks, enhancing robustness through mutual supervision during joint learning. Extensive experiments are conducted using clean test sets extracted from four real-world explicit feedback datasets to validate our denoising method, which demonstrates superior effectiveness across all datasets.

Keywords:

Denoising Recommendation

Robust Recommendation

Semi-supervised Learning

A Semi-supervised Co-training Algorithm for Robust Recommendation

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