338 citations · 454 across the 70 of their papers we have counts for
23 papers · 1 filter
Scaling New Frontiers: Insights into Large Recommendation Models
Wei Guo, Hao Wang, Luankang Zhang +16
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate inc…
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
Tingjia Shen, Hao Wang, Chuhan Wu +7
Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational…
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
Xiang Xu, Hao Wang, Wei Guo +6
Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the…
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
Wenjia Xie, Hao Wang, Luankang Zhang +3
Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic…
MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation
Junxiong Tong, Mingjia Yin, Hao Wang +3
Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance, especially in sparse data or new user scenarios. However, CDR faces challenges s…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Hao Wang, Yongqiang Han, Kefan Wang +6
In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit sema…
UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models
Liu Qi, He Yongyi, Lian Defu +4
Multimodal Entity Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to the referent entities in a multimodal knowledge base, such a…
Analytical and Empirical Study of Herding Effects in Recommendation Systems
Hong Xie, Mingze Zhong, Defu Lian +2
Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, t…
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities
Hong Xie, Jinyu Mo, Defu Lian +2
Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm, a…
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
Ze Liu, Jin Zhang, Chao Feng +3
Although advancements in deep learning have significantly enhanced the recommendation accuracy of deep recommendation models, these methods still suffer from low recommendation eff…
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…
Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang +7
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network…
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation
Tingjia Shen, Hao Wang, Jiaqing Zhang +5
Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Trad…
Dataset Regeneration for Sequential Recommendation
Mingjia Yin, Hao Wang, Wei Guo +5
The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been…
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation
Mingjia Yin, Hao Wang, Wei Guo +6
Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have be…
Understanding Privacy Risks of Embeddings Induced by Large Language Models
Zhihao Zhu, Ninglu Shao, Defu Lian +4
Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations. One promising solution to mitigate these hallucinations is to sto…
WESE: Weak Exploration to Strong Exploitation for LLM Agents
Xu Huang, Weiwen Liu, Xiaolong Chen +5
Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning…
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation
Luankang Zhang, Hao Wang, Suojuan Zhang +5
Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start…
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
Yongqiang Han, Hao Wang, Kefan Wang +6
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user…
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang +4
We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…
Understanding the planning of LLM agents: A survey
Xu Huang, Weiwen Liu, Xiaolong Chen +6
As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This surve…
Securing Recommender System via Cooperative Training
Qingyang Wang, Chenwang Wu, Defu Lian +1
Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. Among existing defense methods, data-processing-based methods inevitably…