With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender …
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph …
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …
K Zhou, H Yu, WX Zhao, JR Wen - … of the ACM web conference 2022, 2022 - dl.acm.org
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference …
Graph representation learning aims to effectively encode high-dimensional sparse graph- structured data into low-dimensional dense vectors, which is a fundamental task that has …
J Chang, C Gao, Y Zheng, Y Hui, Y Niu… - Proceedings of the 44th …, 2021 - dl.acm.org
Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential …
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 …
X Xie, F Sun, Z Liu, S Wu, J Gao… - 2022 IEEE 38th …, 2022 - ieeexplore.ieee.org
Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter …
Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR …