Neural-based multi-task learning has been successfully used in many real-world large-scale
applications such as recommendation systems. For example, in movie recommendations …

In this paper, we introduce a large scale multi-objective ranking system for recommending
what video to watch next on an industrial video sharing platform. The system faces many …

Traditional studies on recommender systems usually leverage only one type of user
behaviors (the optimization target, such as purchase), despite the fact that users also …

The success of recommender systems often depends on their ability to understand and
make use of the context of the recommendation request. Significant research has focused on …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Most existing recommender systems leverage user behavior data of one type, such as the
purchase behavior data in E-commerce. We argue that other types of user behavior data …

Recent studies on recommendation have largely focused on exploring state-of-the-art neural
networks to improve the expressiveness of models, while typically apply the Negative …

Multi-behavior recommendation, which exploits auxiliary behaviors (eg, click and cart) to
help predict users' potential interactions on the target behavior (eg, buy), is regarded as an …

Session-based target behavior prediction aims to predict the next item to be interacted with
specific behavior types (eg, clicking). Although existing methods for session-based behavior …

Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …