Denoising Implicit Feedback for Recommendation (2006.04153v2)

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.

• The paper presents Adaptive Denoising Training (ADT) to tackle noise in implicit feedback, enhancing recommendation accuracy.
• The paper details two paradigms—Truncated Loss and Reweighted Loss—that dynamically modulate noisy data during training.
• The paper validates ADT on datasets like Adressa, Amazon-book, and Yelp, demonstrating significant improvements over conventional methods.

Overview of "Denoising Implicit Feedback for Recommendation"

The paper "Denoising Implicit Feedback for Recommendation" addresses the challenge of noisy implicit feedback in recommender systems, a prevalent issue where interactions such as clicks or purchases do not necessarily reflect user satisfaction. This work is particularly relevant in domains like e-commerce, where users’ actions do not always indicate positive preferences. The authors propose an innovative training strategy that leverages Adaptive Denoising Training (ADT) techniques to mitigate the influence of false-positive interactions on recommendation models.

Problem Context and Motivation

Implicit feedback, due to its ubiquity and volume, serves as a default input for training recommender systems. Nonetheless, it is inherently noisy; for instance, not all clicks on an item translate into purchases, and some purchases can result in returns or negative reviews. The noise in implicit feedback misguides the training of recommendation models, which can degrade their performance by fitting to false user preference patterns. Adjusting to this complexity, prior work has made attempts to account for this noise by incorporating additional feedback or external signals, but these approaches suffer from data sparsity and are not always feasible.

Methodology

The researchers introduce Adaptive Denoising Training (ADT) strategies, aimed at identifying and mitigating the impact of noisy interactions during the learning process. ADT is based on the observed phenomenon that false-positive interactions result in larger loss values in the early stages of training. The paper presents two paradigms within ADT: Truncated Loss and Reweighted Loss. These paradigms are designed to dynamically adjust the contribution of potentially noisy data points during model training:

1. Truncated Loss: This approach removes interactions with loss values exceeding a dynamically adjusted threshold. The threshold evolves with training iterations and is flexibly set based on a tunable drop rate.
2. Reweighted Loss: This approach assigns smaller weights to interactions with large losses, thereby reducing their influence on the learned model parameters.

Both techniques are instantiated on the binary cross-entropy loss and are applicable across various neural recommendation models without reliance on external, supplementary feedback data.

Experimental Validation

The efficacy of ADT strategies was empirically validated using three large-scale datasets: Adressa, Amazon-book, and Yelp. These datasets span different domains and interaction types, providing a broad testbed for the ADT strategies' capabilities. Three recommendation models were used to evaluate performance: Generalized Matrix Factorization (GMF), Neural Matrix Factorization (NeuMF), and Collaborative Denoising Auto-Encoder (CDAE). The results demonstrated significant improvements in recommendation quality when models were trained with ADT strategies compared to conventional methods.

Implications and Future Directions

The paper's findings underscore the potential of employing model-based denoising methods to enhance recommender systems' robustness without relying on additional data sources. From a practical standpoint, this approach may improve user experience by ensuring more relevant item recommendations, ultimately enhancing user satisfaction and engagement. Theoretically, the work contributes to the understanding of how noise manifests in model training and suggests adaptive strategies as a viable path forward in improving recommendation systems.

Future research could explore applications of ADT to other types of recommendation loss functions and further refine the technique to adaptively tune user-specific or item-specific parameters, thus broadening the utility and effectiveness of denoising strategies. Additionally, ADT could extend to broader machine learning contexts where similar types of implicit noisy feedback are prevalent.