Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

Automatic tagging on software information sites is a tag recommendation service. It aims to
recommend content-based tags for a software object to help developers make distinctions …

With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social recommendation …

Methods for bandit learning from user interactions often require a model of the reward a
certain context-action pair will yield–for example, the probability of a click on a …

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to
handle the recommendation problem under cold-start settings. Specifically, we divide the …

Modern recommender systems are often modelled under the sequential decision-making
paradigm, where the system decides which recommendations to show in order to maximise …

We focus on Maximum Inner Product Search (MIPS), which is an essential problem in many
machine learning communities. Given a query, MIPS finds the most similar items with the …

In recommender systems, click behaviors play a fundamental role in mining users' interests
and training models (clicked items as positive samples). Such signals are implicit feedback …

News recommender systems have become an effective manner to help users make
decisions by suggesting the potential news that users may click and read, which has shown …

Off-policy learning has drawn huge attention in recommender systems (RS), which provides
an opportunity for reinforcement learning to abandon the expensive online training …