Denoising Implicit Feedback for Cold-start Recommendation

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Information Retrieval · Depth: Expert, quick

Summary

DIF, a model-agnostic denoising method, addresses the challenge of noisy implicit feedback in cold-start recommendation scenarios, where new items are particularly susceptible to issues like clickbait and position bias. Traditional denoising approaches, often based on heuristic patterns or loss values, prove ineffective for cold items. DIF infers pseudo-labels indicating user interest in cold items by leveraging content-similar warm items. It enhances pseudo-label accuracy by modeling confidence based on content similarity and aggregating multiple labels. Furthermore, DIF explicitly estimates noisy sample label uncertainty using relative entropy and item cold-start status, adaptively guiding pseudo-label correction. Deployed on Kuaishou, a billion-user short video application, DIF has demonstrated significant improvements in commercial metrics within cold-start contexts, supported by theoretical justification and extensive real-world experiments.

Key takeaway

For Machine Learning Engineers building recommender systems and struggling with cold-start item performance, you should investigate methods like DIF. This approach effectively mitigates noisy implicit feedback for new items by using content-based pseudo-labeling and uncertainty estimation. Implementing such techniques can significantly improve recommendation quality and commercial metrics for your cold-start inventory, as demonstrated by its deployment on Kuaishou.

Key insights

DIF denoises implicit feedback for cold-start recommendations by inferring content-based pseudo-labels and estimating label uncertainty.

Principles

• Cold items are more prone to noisy implicit feedback.
• User preferences for content remain stable.
• Pseudo-label confidence can be modeled via content similarity.

Method

Infer pseudo-labels for cold items from content-similar warm items, model pseudo-label confidence, aggregate multiple pseudo-labels, and estimate noisy sample label uncertainty to guide corrections.

In practice

• Leverage content similarity to infer user interest for new items.
• Model pseudo-label confidence for improved pseudo-label accuracy.
• Estimate label uncertainty to adaptively correct noisy labels.

Topics

• Implicit Feedback
• Cold-start Recommendation
• Denoising
• Recommender Systems
• Pseudo-labeling
• Uncertainty Estimation
• Kuaishou

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.