1 code implementation • 18 Jul 2023 • Rui Zhang, Yixin Su, Bayu Distiawan Trisedya, Xiaoyan Zhao, Min Yang, Hong Cheng, Jianzhong Qi
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
1 code implementation • 28 Jun 2022 • Yixin Su, Yunxiang Zhao, Sarah Erfani, Junhao Gan, Rui Zhang
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions.
1 code implementation • 10 May 2021 • Yixin Su, Rui Zhang, Sarah Erfani, Junhao Gan
User and item attributes are essential side-information; their interactions (i. e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems.
4 code implementations • 2 Aug 2020 • Yixin Su, Rui Zhang, Sarah Erfani, Zhenghua Xu
To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.
no code implementations • 3 Aug 2019 • Yixin Su, Sarah Monazam Erfani, Rui Zhang
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry.