With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems …
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we …
K Mao, J Zhu, X Xiao, B Lu, Z Wang, X He - Proceedings of the 30th ACM …, 2021 - dl.acm.org
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of …
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged …
F Liu, Z Cheng, L Zhu, Z Gao, L Nie - Proceedings of the web conference …, 2021 - dl.acm.org
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the …
Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …
Z Fan, Z Liu, Y Wang, A Wang, Z Nazari… - Proceedings of the …, 2022 - dl.acm.org
Sequential recommendation models the dynamics of a user's previous behaviors in order to forecast the next item, and has drawn a lot of attention. Transformer-based approaches …
Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer …