As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …

Accurate estimation of post-click conversion rate is critical for building recommender
systems, which has long been confronted with sample selection bias and data sparsity …

Recommender systems can help correlate information and recommend personalised
services to users as a general information filtering tool. However, contextual factors …

We propose a simple but effective method to recommend exercises with high quality and
diversity for students. Our method is made up of three key components:(1) candidate …

Evaluating recommendation systems is a task of utmost importance and a very active
research field. While online evaluation is the most reliable evaluation procedure, it may also …

In modern machine learning, users often have to collaborate to learn distributions that
generate the data. Communication can be a significant bottleneck. Prior work has studied …

As a basic research problem for building effective recommender systems, post-click
conversion rate (CVR) estimation has long been plagued by sample selection bias and data …

Web applications utilize Recommendation Systems (RS) to address the problem of
consumer over-choices. Recent works have taken advantage of multi-modality or multi-view …

Neural network-based collaborative filtering systems focus on designing network
architectures to learn better representations while fixing the input to the user/item interaction …

Personalized question recommendation for students is an important research topic in the
field of smart education. Current studies depend on collaborative filtering based, cognitive …