6 citations · 17 across the 17 of their papers we have counts for
18 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…
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents
Weizhi Zhang, Xinyang Zhang, Chenwei Zhang +12
Large Language Model (LLM) empowered agents have recently emerged as advanced paradigms that exhibit impressive capabilities in a wide range of domains and tasks. Despite their pot…
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…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…
Item Cluster-aware Prompt Learning for Session-based Recommendation
Wooseong Yang, Chen Wang, Zihe Song +2
Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly o…
Personalized Multi-task Training for Recommender System
Liangwei Yang, Zhiwei Liu, Jianguo Zhang +5
In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of choices aligned with their preferences. These s…
Taxonomy-Guided Zero-Shot Recommendations with LLMs
Yueqing Liang, Liangwei Yang, Chen Wang +3
With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise. However, we…
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…
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
Chaoguang Luo, Liuying Wen, Yong Qin +3
Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numero…
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
Yu Wang, Zhiwei Liu, Jianguo Zhang +3
The rise of Large Language Models (LLMs) has sparked interest in their application to sequential recommendation tasks as they can provide supportive item information. However, due…
Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering
Ke Xu, Yuanjie Zhu, Weizhi Zhang +1
Graph Convolution Networks (GCNs) are widely considered state-of-the-art for collaborative filtering. Although several GCN-based methods have been proposed and achieved state-of-th…
Multi-view Graph Convolution for Participant Recommendation
Xiaolong Liu, Liangwei Yang, Chen Wang +3
Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable comme…
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form g…
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…
Collaborative Semantic Alignment in Recommendation Systems
Chen Wang, Liangwei Yang, Zhiwei Liu +4
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced ri…