Jianhuang Lai

BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

2021 · arXiv (Cornell University)

Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning tries to learn a …

G$^3$SR: Global Graph Guided Session-based Recommendation

2022 · arXiv (Cornell University)

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each …

Vision Transformer for Contrastive Clustering

2022 · arXiv (Cornell University)

Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation learning. Besides ViT, contrastive learning is another popular research topic recently. …

Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

2018 · IEEE Transactions on Cybernetics

Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, …

A Study of Reinforcement Learning for Neural Machine Translation

2018

Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world …