In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by …
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences …
With tremendous amount of recommendation algorithms proposed every year, one critical issue has attracted a considerable amount of attention: there are no effective benchmarks for …
H Fang, D Zhang, Y Shu, G Guo - ACM Transactions on Information …, 2020 - dl.acm.org
In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as …
H Tang, G Zhao, X Bu, X Qian - Knowledge-Based Systems, 2021 - Elsevier
The recommendation system is an important and widely used technology in the era of Big Data. Current methods have fused side information into it to alleviate the sparsity problem …
Y Liu, S Yang, Y Xu, C Miao, M Wu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency …
Q Guo, Z Sun, J Zhang, YL Theng - … of the AAAI Conference on artificial …, 2020 - aaai.org
Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most …
X Sha, Z Sun, J Zhang - Electronic Commerce Research and Applications, 2021 - Elsevier
Abstract Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and …
Side information of items, eg, images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training …