Shuyuan Xu
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View article: Dynamic changes of dopamine neuron activity and plasticity at different stages of negative reinforcement learning
Dynamic changes of dopamine neuron activity and plasticity at different stages of negative reinforcement learning Open
Research indicates that midbrain dopaminergic neurons encode reward prediction error (RPE) signals involved in positive reinforcement learning. However, studies on dopamine’s role in negative reinforcement learning (NRL) are scarce. Learni…
View article: Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys
Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys Open
View article: Causal Inference for Recommendation: Foundations, Methods, and Applications
Causal Inference for Recommendation: Foundations, Methods, and Applications Open
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, …
View article: Yau-YauAL: A computer tool for solving nonlinear filtering problems
Yau-YauAL: A computer tool for solving nonlinear filtering problems Open
View article: Causal Structure Learning for Recommender System
Causal Structure Learning for Recommender System Open
A fundamental challenge of recommender systems (RS) is understanding the causal dynamics underlying users’ decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However,…
View article: OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems Open
View article: IDGenRec: LLM-RecSys Alignment with Textual ID Learning
IDGenRec: LLM-RecSys Alignment with Textual ID Learning Open
View article: AutoFlow: Automated Workflow Generation for Large Language Model Agents
AutoFlow: Automated Workflow Generation for Large Language Model Agents Open
Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural language. One important application of LLM is LLM-based AI Agent, which leverages the ability of LLM as well as external t…
View article: AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents
AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents Open
Since their inception, programming languages have trended towards greater readability and lower barriers for programmers. Following this trend, natural language can be a promising type of programming language that provides great flexibilit…
View article: A Survey on Trustworthy Recommender Systems
A Survey on Trustworthy Recommender Systems Open
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS…
View article: IDGenRec: LLM-RecSys Alignment with Textual ID Learning
IDGenRec: LLM-RecSys Alignment with Textual ID Learning Open
Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However, in contrast to standard NLP tasks that inherently operate…
View article: AIOS: LLM Agent Operating System
AIOS: LLM Agent Operating System Open
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation…
View article: Comparison of Multiple Machine Learning Methods for Correcting Groundwater Levels Predicted by Physics-Based Models
Comparison of Multiple Machine Learning Methods for Correcting Groundwater Levels Predicted by Physics-Based Models Open
Accurate groundwater level (GWL) prediction is crucial in groundwater resource management. Currently, it relies mainly on physics-based models for prediction and quantitative analysis. However, physics-based models used for prediction ofte…
View article: UP5: Unbiased Foundation Model for Fairness-aware Recommendation
UP5: Unbiased Foundation Model for Fairness-aware Recommendation Open
View article: LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem Open
This paper envisions a revolutionary AIOS-Agent ecosystem, where Large Language Model (LLM) serves as the (Artificial) Intelligent Operating System (IOS, or AIOS)--an operating system "with soul". Upon this foundation, a diverse range of L…
View article: Language is All a Graph Needs
Language is All a Graph Needs Open
The emergence of large-scale pre-trained language models has revolutionized various AI research domains. Transformers-based Large Language Models (LLMs) have gradually replaced CNNs and RNNs to unify fields of computer vision and natural l…
View article: Efficient Non-Sampling Graph Neural Networks
Efficient Non-Sampling Graph Neural Networks Open
A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very bas…
View article: GenRec: Large Language Model for Generative Recommendation
GenRec: Large Language Model for Generative Recommendation Open
In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively une…
View article: Deconfounded Causal Collaborative Filtering
Deconfounded Causal Collaborative Filtering Open
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usuall…
View article: OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems Open
In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers. Despite this interest, the field is still emerging, and the lack of open-source R&D p…
View article: UP5: Unbiased Foundation Model for Fairness-aware Recommendation
UP5: Unbiased Foundation Model for Fairness-aware Recommendation Open
Recent advances in Foundation Models such as Large Language Models (LLMs) have propelled them to the forefront of Recommender Systems (RS). Despite their utility, there is a growing concern that LLMs might inadvertently perpetuate societal…
View article: Tutorials at The Web Conference 2023
Tutorials at The Web Conference 2023 Open
Social networks have been widely studied over the last century from multiple\ndisciplines to understand societal issues such as inequality in employment\nrates, managerial performance, and epidemic spread. Today, these and many more\nissue…
View article: OpenAGI: When LLM Meets Domain Experts
OpenAGI: When LLM Meets Domain Experts Open
Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for comple…
View article: Causal Inference for Recommendation: Foundations, Methods and Applications
Causal Inference for Recommendation: Foundations, Methods and Applications Open
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, …
View article: Causal Structure Learning with Recommendation System
Causal Structure Learning with Recommendation System Open
A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. Howev…
View article: A Survey on Trustworthy Recommender Systems
A Survey on Trustworthy Recommender Systems Open
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS…
View article: Hemispheric Processing of Chinese Scientific Metaphors: Evidence via Hemifield Presentation
Hemispheric Processing of Chinese Scientific Metaphors: Evidence via Hemifield Presentation Open
The role of the two hemispheres in processing metaphoric language is controversial. In order to complement current debates, the current divided visual field (DVF) study introduced scientific metaphors as novel metaphors, presenting orienta…
View article: Fairness in Recommendation: Foundations, Methods and Applications
Fairness in Recommendation: Foundations, Methods and Applications Open
As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the qualit…
View article: Deconfounded Causal Collaborative Filtering
Deconfounded Causal Collaborative Filtering Open
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usuall…
View article: Counterfactual Evaluation for Explainable AI
Counterfactual Evaluation for Explainable AI Open
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanati…