While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Recommender systems (RS) have demonstrated great success in information seeking.
Recent years have witnessed a large number of work on inventing recommendation models …

Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative
filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing …

In the absence of user profile information, recommender systems have to only rely on current
session information for recommendation. E-commerce sites may use transitions between …

Duration bias widely exists in video recommendations, where models tend to recommend
short videos for the higher ratio of finish playing and thus possibly fail to capture users' true …

With the proliferation of short video applications, the significance of short video
recommendations has vastly increased. Unlike other recommendation scenarios, short …

Recommender systems are prone to be misled by biases in the data. Models trained with
biased data fail to capture the real interests of users, thus it is critical to alleviate the impact …

The recommender system has gained research attention from education research
communities mainly due to two main reasons: increasing needs for personalized learning …

Recommendation algorithms are susceptible to popularity bias: a tendency to recommend
popular items even when they fail to meet user needs. A related issue is that the …

Implicit feedback (eg, user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …