70 citations · 106 across the 6 of their papers we have counts for
6 papers
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
Guoqiang Sun, Yibin Shen, Sijin Zhou +6
Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between do…
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
Yuanliang Zhang, Xiaofeng Wang, Jinxin Hu +3
Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enj…
Enhancing Sequential Recommendation with Graph Contrastive Learning
Yixin Zhang, Yong Liu, Yonghui Xu +5
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exp…
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
Chenyi Lei, Shixian Luo, Yong Liu +6
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content. However, it is still very challenging to pre-train neural models f…
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Yong Liu, Susen Yang, Chenyi Lei +5
Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training…
Comparative Deep Learning of Hybrid Representations for Image Recommendations
Chenyi Lei, Dong Liu, Weiping Li +2
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such r…