Denoising Implicit Feedback for Recommendation

Abstract

The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue, the downside is that they are not as clean in reflecting the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of critical importance to account for the inevitable noises in implicit feedback for recommender training. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender training. We find serious negative impacts of noisy implicit feedback,i.e., fitting the noisy data prevents the recommender from learning the actual user preference. Our target is to identify and prune noisy interactions, so as to improve the quality of recommender training. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy namedAdaptive Denoising Training(ADT), which adaptively prunes noisy interactions during training. Specifically, we devise two paradigms for adaptive loss formulation: Truncated Loss that discards the large-loss samples with a dynamic threshold in each iteration; and reweighted Loss that adaptively lowers the weight of large-loss samples. We instantiate the two paradigms on the widely used binary cross-entropy loss and test the proposed ADT strategies on three representative recommenders. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training.

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Graph Collaborative Filtering
• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
• Understanding deep learning requires rethinking generalization
• A Closer Look at Memorization in Deep Networks
• MMALFM: Explainable Recommendation by Leveraging Reviews and Images
• How does Disagreement Help Generalization against Label Corruption?
• To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
• LambdaOpt: Learn to Regularize Recommender Models in Finer Levels

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• Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
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• Self-Guided Learning to Denoise for Robust Recommendation
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Market2Dish: Health-aware Food Recommendation
• Learning Robust Recommenders through Cross-Model Agreement
• Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
• AutoDenoise: Automatic Data Instance Denoising for Recommendations
• Knowledge-refined Denoising Network for Robust Recommendation
• Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
• Augmented Negative Sampling for Collaborative Filtering
• Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
• What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
• Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
• LightGNN: Simple Graph Neural Network for Recommendation
• Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
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• Dual-interest Factorization-heads Attention for Sequential Recommendation
• Unleashing the Power of Large Language Model for Denoising Recommendation
• Integrating Item Relevance in Training Loss for Sequential Recommender Systems
• Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias
• LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
• Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
• Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning
• Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System
• Adversarial Collaborative Filtering for Free
• Shapley Value-driven Data Pruning for Recommender Systems
• Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
• Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
• Recommendation Is a Dish Better Served Warm
• Learning Robust Recommender from Noisy Implicit Feedback
• ConQueR: Contextualized Query Reduction using Search Logs
• Knowledge Graph-enhanced Sampling for Conversational Recommender System
• Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback
• Empowering Denoising Sequential Recommendation with Large Language Model Embeddings
• When the Label Ignores the Request: Auditing Policy-Selected Targets in Synthetic Conversational Music Recommendation
• Enhancing Recommendation with Denoising Auxiliary Task
• Which Samples Should be Learned First: Easy or Hard?