8 citations · 12 across the 9 of their papers we have counts for
9 papers
A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
Hao Zhang, Mingyue Cheng, Qi Liu +5
Recommender systems (RS) have become crucial tools for information filtering in various real world scenarios. And cross domain recommendation (CDR) has been widely explored in rece…
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Hao Zhang, Mingyue Cheng, Qi Liu +3
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction…
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Li Li, Mingyue Cheng, Zhiding Liu +3
Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ…
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Mingyue Cheng, Hao Zhang, Jiqian Yang +7
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Mingyue Cheng, Xiaoyu Tao, Qi Liu +3
Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing…
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
Mingyue Cheng, Hao Zhang, Qi Liu +6
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{co…
Unlocking the Potential of Large Language Models for Explainable Recommendations
Yucong Luo, Mingyue Cheng, Hao Zhang +3
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enha…
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Hao Zhang, Mingyue Cheng, Zhiding Liu +1
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a com…
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Junzhe Jiang, Shang Qu, Mingyue Cheng +8
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the…