Recommender systems (RSs) have been the most important technology for increasing the
business in Taobao, the largest online consumer-to-consumer (C2C) platform in China …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

In very sparse recommender data sets, attributes of users such as age, gender and home
location and attributes of items such as, in the case of movies, genre, release year, and …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

We present collaborative similarity embedding (CSE), a unified framework that exploits
comprehensive collaborative relations available in a user-item bipartite graph for …

Most of heterogeneous information network (HIN) based recommendation models are based
on the user and item modeling with meta-paths. However, they always model users and …

Learned embeddings for products are an important building block for web-scale e-
commerce recommendation systems. At Pinterest, we build a single set of product …

Personalized recommendation plays an important role in many online services. Substantial
research has been dedicated to learning embeddings of users and items to predict a user's …

Traditional recommendation techniques are hindered by the simplicity and sparsity of user-
item interaction data and can be improved by introducing auxiliary information related to …

The remarkable progress of network embedding has led to state-of-the-art algorithms in
recommendation. However, the sparsity of user-item interactions (ie, explicit preferences) on …