Recommending purely cold-start items is a long-standing and fundamental challenge in the
recommender systems. Without any historical interaction on cold-start items, the …

Learning dynamic user preference has become an increasingly important component for
many online platforms (eg, video-sharing sites, e-commerce systems) to make sequential …

One of the important factors affecting micro-video recommender systems is to model the
multi-modal user preference on the micro-video. Despite the remarkable performance of …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …

The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …

Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …

The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …

Micro-videos have recently gained immense popularity, sparking critical research in micro-
video recommendation with significant implications for the entertainment, advertising, and e …