Yunshan Ma

Causal Disentangled Recommendation against User Preference Shifts

2023 · ACM Transactions on Information Systems

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on …

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

2023 · arXiv (Cornell University)

Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the …

Enhancing Item-level Bundle Representation for Bundle Recommendation

2023 · arXiv (Cornell University)

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to …

Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding

2024 · arXiv (Cornell University)

The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events. We refer to the complex events composed of many news articles …