Self-guided learning to denoise for robust recommendation
The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …
recommender systems. Generally speaking, observed interactions are considered as …
Denoising implicit feedback for recommendation
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 …
recommender systems. While the large volume of implicit feedback alleviates the data …
Efficient bi-level optimization for recommendation denoising
The acquisition of explicit user feedback (eg, ratings) in real-world recommender systems is
often hindered by the need for active user involvement. To mitigate this issue, implicit …
often hindered by the need for active user involvement. To mitigate this issue, implicit …
Curriculum disentangled recommendation with noisy multi-feedback
Learning disentangled representations for user intentions from multi-feedback (ie, positive
and negative feedback) can enhance the accuracy and explainability of recommendation …
and negative feedback) can enhance the accuracy and explainability of recommendation …
Adversarial collaborative neural network for robust recommendation
Most of recent neural network (NN)-based recommendation techniques mainly focus on
improving the overall performance, such as hit ratio for top-N recommendation, where the …
improving the overall performance, such as hit ratio for top-N recommendation, where the …
SLED: Structure Learning based Denoising for Recommendation
In recommender systems, click behaviors play a fundamental role in mining users' interests
and training models (clicked items as positive samples). Such signals are implicit feedback …
and training models (clicked items as positive samples). Such signals are implicit feedback …
Invariant preference learning for general debiasing in recommendation
Current recommender systems have achieved great successes in online services, such as E-
commerce and social media. However, they still suffer from the performance degradation in …
commerce and social media. However, they still suffer from the performance degradation in …
Modeling dynamic missingness of implicit feedback for recommendation
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is
well known that implicit feedback contains a large number of values that are\emph {missing …
well known that implicit feedback contains a large number of values that are\emph {missing …
Learning recommenders for implicit feedback with importance resampling
Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers
from the lack of negative samples, which has a significant impact on the training of …
from the lack of negative samples, which has a significant impact on the training of …
Rectifying unfairness in recommendation feedback loop
The issue of fairness in recommendation systems has recently become a matter of growing
concern for both the academic and industrial sectors due to the potential for bias in machine …
concern for both the academic and industrial sectors due to the potential for bias in machine …