6 citations · 9 across the 7 of their papers we have counts for
7 papers
Personalized Recommendation Tool Learning via Autonomous Language Agents
Mingdai Yang, Zhiwei Liu, Weizhi Zhang +3
Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-ba…
Training Large Recommendation Models via Graph-Language Token Alignment
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, trad…
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Yuwei Cao, Liangwei Yang, Zhiwei Liu +5
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address thi…
Instruction-based Hypergraph Pretraining
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or…
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Zhongfen Deng, Hao Peng, Tao Zhang +4
Product attribute value extraction is an important task in e-Commerce which can help several downstream applications such as product search and recommendation. Most previous models…
Unified Pretraining for Recommendation via Task Hypergraphs
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging…
Knowledge Graph Context-Enhanced Diversified Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of acc…