49 citations · 108 across the 7 of their papers we have counts for
7 papers · 1 filter
OpenSiteRec: An Open Dataset for Site Recommendation
Xinhang Li, Xiangyu Zhao, Yejing Wang +5
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic d…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…
AutoDenoise: Automatic Data Instance Denoising for Recommendations
Weilin Lin, Xiangyu Zhao, Yejing Wang +2
Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recomm…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
Multimodal Recommender Systems: A Survey
Qidong Liu, Jiaxi Hu, Yutian Xiao +5
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier an…
Multi-Task Deep Recommender Systems: A Survey
Yuhao Wang, Ha Tsz Lam, Yi Wong +8
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic i…
AutoField: Automating Feature Selection in Deep Recommender Systems
Yejing Wang, Xiangyu Zhao, Tong Xu +1
Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most…