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 …

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 often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

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 …

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 …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this article, we first describe the generation process of the biased and …

Learning disentangled representations for user intentions from multi-feedback (ie, positive
and negative feedback) can enhance the accuracy and explainability of recommendation …

In recommender systems, users' behavior data are driven by the interactions of user-item
latent factors. To improve recommendation effectiveness and robustness, recent advances …

As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

For better user satisfaction and business effectiveness, more and more attention has been
paid to the sequence-based recommendation system, which is used to infer the evolution of …