A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …

Accurate user and item embedding learning is crucial for modern recommender systems.
However, most existing recommendation techniques have thus far focused on modeling …

Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …

Multimedia recommendation methods aim to discover the user preference on the multi-
modal information to enhance the collaborative filtering (CF) based recommender system …

A well-designed recommender system can accurately capture the attributes of users and
items, reflecting the unique preferences of individuals. Traditional recommendation …

Session-based recommendation plays a central role in a wide spectrum of online
applications, ranging from e-commerce to online advertising services. However, the majority …

Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …

In most real-world recommender systems, users interact with items in a sequential and multi-
behavioral manner. Exploring the fine-grained relationship of items behind the users' multi …

This paper delves into the problem of correlated time-series forecasting in practical
applications, an area of growing interest in a multitude of fields such as stock price …