Search Results for author: Juntao Tan

Found 20 papers, 10 papers with code

LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem

no code implementations6 Dec 2023 Yingqiang Ge, Yujie Ren, Wenyue Hua, Shuyuan Xu, Juntao Tan, Yongfeng Zhang

We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts: LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level).

Language Modelling Large Language Model

User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

1 code implementation2 Aug 2023 Juntao Tan, Yingqiang Ge, Yan Zhu, Yinglong Xia, Jiebo Luo, Jianchao Ji, Yongfeng Zhang

Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability.

counterfactual Counterfactual Reasoning +1

GenRec: Large Language Model for Generative Recommendation

1 code implementation2 Jul 2023 Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, Yongfeng Zhang

Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics.

Language Modelling Large Language Model +1

Counterfactual Collaborative Reasoning

no code implementations30 Jun 2023 Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang

In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models.

counterfactual Counterfactual Reasoning +3

VIP5: Towards Multimodal Foundation Models for Recommendation

1 code implementation23 May 2023 Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, Yongfeng Zhang

In light of this, we propose the development of a multimodal foundation model (MFM) considering visual, textual, and personalization modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5), to unify various modalities and recommendation tasks.

Recommendation Systems

Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training

no code implementations11 Apr 2023 Juntao Tan, Shelby Heinecke, Zhiwei Liu, Yongjun Chen, Yongfeng Zhang, Huan Wang

Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information.

Sequential Recommendation

OpenAGI: When LLM Meets Domain Experts

1 code implementation NeurIPS 2023 Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li, Yongfeng Zhang

This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI).

Benchmarking Natural Language Queries

ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

1 code implementation27 Jan 2023 Juntao Tan, Yongfeng Zhang

This paper presents ExplainableFold, an explainable AI framework for protein structure prediction.

counterfactual Protein Structure Prediction

Causal Inference for Recommendation: Foundations, Methods and Applications

no code implementations8 Jan 2023 Shuyuan Xu, Jianchao Ji, Yunqi Li, Yingqiang Ge, Juntao Tan, Yongfeng Zhang

We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.

Causal Inference Fairness +1

Dynamic Causal Collaborative Filtering

1 code implementation23 Aug 2022 Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, Yongfeng Zhang

As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems.

Collaborative Filtering counterfactual +2

A Survey on Trustworthy Recommender Systems

no code implementations25 Jul 2022 Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang

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.

Decision Making Explainable Recommendation +2

Fairness in Recommendation: Foundations, Methods and Applications

no code implementations26 May 2022 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang

It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.

Decision Making Fairness +1

Residue-based Label Protection Mechanisms in Vertical Logistic Regression

no code implementations9 May 2022 Juntao Tan, Lan Zhang, Yang Liu, Anran Li, Ye Wu

To deal with this, we then propose three protection mechanisms, e. g., additive noise mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages local differential privacy and homomorphic encryption techniques, to prevent the attack and improve the robustness of the vertical logistic regression.

Federated Learning Inference Attack +1

Explainable Fairness in Recommendation

no code implementations24 Apr 2022 Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang

In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.

counterfactual Fairness +1

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

1 code implementation17 Feb 2022 Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, Yongfeng Zhang

For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.

Causal Inference counterfactual

Deconfounded Causal Collaborative Filtering

1 code implementation14 Oct 2021 Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, Yongfeng Zhang

Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.

Collaborative Filtering Recommendation Systems

Counterfactual Evaluation for Explainable AI

no code implementations5 Sep 2021 Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, Yongfeng Zhang

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 explanation -- is still an open problem.

counterfactual Counterfactual Reasoning

Counterfactual Explainable Recommendation

2 code implementations24 Aug 2021 Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang

Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.

Causal Inference counterfactual +5

A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs

1 code implementation10 Nov 2020 Juntao Tan, Changkyu Song, Abdeslam Boularias

The triplet examples are finally used to train a siamese neural network that projects the generic visual features into a low-dimensional manifold.

Clustering object-detection +2

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