Automated Data Denoising for Recommendation

by   Yingqiang Ge, et al.

In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data sparsity, implicit feedback is often the default choice for training recommender systems (RS), however, such data could be very noisy due to the randomness and diversity of user behaviors. For instance, a large portion of clicks may not reflect true user preferences and many purchases may result in negative reviews or returns. Fortunately, by utilizing the strengths of both types of feedback to compensate for the weaknesses of the other, we can mitigate the above issue at almost no cost. In this work, we propose an Automated Data Denoising framework, AutoDenoise, for recommendation, which uses a small number of explicit data as validation set to guide the recommender training. Inspired by the generalized definition of curriculum learning (CL), AutoDenoise learns to automatically and dynamically assign the most appropriate (discrete or continuous) weights to each implicit data sample along the training process under the guidance of the validation performance. Specifically, we use a delicately designed controller network to generate the weights, combine the weights with the loss of each input data to train the recommender system, and optimize the controller with reinforcement learning to maximize the expected accuracy of the trained RS on the noise-free validation set. Thorough experiments indicate that AutoDenoise is able to boost the performance of the state-of-the-art recommendation algorithms on several public benchmark datasets.


Denoising Implicit Feedback for Recommendation

The ubiquity of implicit feedback makes them the default choice to build...

AutoDenoise: Automatic Data Instance Denoising for Recommendations

Historical user-item interaction datasets are essential in training mode...

Learning Robust Recommender from Noisy Implicit Feedback

The ubiquity of implicit feedback makes it indispensable for building re...

Probabilistic and Variational Recommendation Denoising

Learning from implicit feedback is one of the most common cases in the a...

Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems

In this paper, we propose a robust sequential learning strategy for trai...

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

Recommender systems that learn from implicit feedback often use large vo...

Quick Graph Conversion for Robust Recommendation

Implicit feedback plays a huge role in recommender systems, but its high...

Please sign up or login with your details

Forgot password? Click here to reset