75 citations · 473 across the 72 of their papers we have counts for
12 papers · 1 filter
Few-shot News Recommendation via Cross-lingual Transfer
Taicheng Guo, Lu Yu, Basem Shihada +1
The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to…
AppQ: Warm-starting App Recommendation Based on View Graphs
Dan Su, Jiqiang Liu, Sencun Zhu +3
Current app ranking and recommendation systems are mainly based on user-generated information, e.g., number of downloads and ratings. However, new apps often have few (or even no)…
Scientific Dataset Discovery via Topic-level Recommendation
Basmah Altaf, Shichao Pei, Xiangliang Zhang
Data intensive research requires the support of appropriate datasets. However, it is often time-consuming to discover usable datasets matching a specific research topic. We formula…
Socially-Aware Self-Supervised Tri-Training for Recommendation
Junliang Yu, Hongzhi Yin, Min Gao +3
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based me…
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
Tong Chen, Hongzhi Yin, Xiangliang Zhang +3
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manu…
Fast-adapting and Privacy-preserving Federated Recommender System
Qinyong Wang, Hongzhi Yin, Tong Chen +3
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Rece…
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
Lei Guo, Hongzhi Yin, Tong Chen +2
In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group memb…
Graph Embedding for Recommendation against Attribute Inference Attacks
Shijie Zhang, Hongzhi Yin, Tong Chen +3
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be natur…
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models explo…
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent…
Graph Factorization Machines for Cross-Domain Recommendation
Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3
Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…
Addressing Class-Imbalance Problem in Personalized Ranking
Lu Yu, Shichao Pei, Chuxu Zhang +4
Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{…