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 …

Implicit feedback is widely used in collaborative filtering methods for sequential
recommendation. It is well known that implicit feedback contains a large number of values …

In this paper, we propose a novel ranking framework for collaborative filtering with the
overall aim of learning user preferences over items by minimizing a pairwise ranking loss …

Recommender systems that learn from implicit feedback often use large volumes of a single
type of implicit user feedback, such as clicks, to enhance the prediction of sparse target …

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 …

In many collaborative filtering (CF) applications, latent approaches are the preferred model
choice due to their ability to generate real-time recommendations efficiently. However, the …

One-hot encoder accompanied by a softmax loss has become the default configuration to
deal with the multiclass problem, and is also prevalent in deep learning (DL) based …

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 …

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

In recent years, interest in recommender research has shifted from explicit feedback towards
implicit feedback data. A diversity of complex models has been proposed for a wide variety …