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 …

The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …

In recommendation systems, the choice of loss function is critical since a good loss may
significantly improve the model performance. However, manually designing a good loss is a …

Recommender systems widely use implicit feedback such as click data because of its
general availability. Although the presence of clicks signals the users' preference to some …

Implicit-feedback Recommenders (ImplicitRec) leverage positive only user-item interactions,
such as clicks, to learn personalized user preferences. Recommenders are often evaluated …

Personalized recommendation has become increasingly pervasive nowadays. Users
receive recommendations on products, movies, point-of-interests and other online services …

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 …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

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

Designing an effective loss function plays a crucial role in training deep recommender
systems. Most existing works often leverage a predefined and fixed loss function that could …