Search Results for author: Yongfeng Zhang

Found 129 papers, 70 papers with code

Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models

1 code implementation15 Jul 2024 Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, ZiHao Zhou, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, Felix Juefei-Xu, Kaize Ding, Fan Yang, Ruixiang Tang, Yongfeng Zhang

We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.

Backdoor Attack Multiple-choice

VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models

no code implementations5 Jul 2024 Hang Gao, Yongfeng Zhang

This establishes the profound difficulty of pursuing similarity and diversity simultaneously in vector retrieval and lays a theoretical groundwork for further research.

Combinatorial Optimization Diversity +1

AutoFlow: Automated Workflow Generation for Large Language Model Agents

no code implementations1 Jul 2024 Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, Yongfeng Zhang

We believe that the automatic generation and interpretation of workflows in natural language represent a promising paradigm for solving complex tasks, particularly with the rapid development of LLMs.

MoralBench: Moral Evaluation of LLMs

1 code implementation6 Jun 2024 Jianchao Ji, Yutong Chen, Mingyu Jin, Wujiang Xu, Wenyue Hua, Yongfeng Zhang

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing to decision-making support systems.


Information Maximization via Variational Autoencoders for Cross-Domain Recommendation

no code implementations31 May 2024 Xuying Ning, Wujiang Xu, Xiaolei Liu, Mingming Ha, Qiongxu Ma, Youru Li, Linxun Chen, Yongfeng Zhang

We also propose a Generative Recommendation Framework combined with three regularizers inspired by the mutual information maximization (MIM) theory \cite{mcgill1954multivariate} to capture the semantic differences between a user's interests shared across domains and those specific to certain domains, as well as address the informational gap between a user's actual interaction sequences and the pseudo-sequences generated.

Denoising Disentanglement +1

SLMRec: Empowering Small Language Models for Sequential Recommendation

no code implementations28 May 2024 Wujiang Xu, Zujie Liang, Jiaojiao Han, Xuying Ning, Wenfang Lin, Linxun Chen, Feng Wei, Yongfeng Zhang

The sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions.

Knowledge Distillation Language Modelling +3

AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents

3 code implementations11 May 2024 Shuyuan Xu, Zelong Li, Kai Mei, Yongfeng Zhang

However, the inherent vagueness, ambiguity, and verbosity of natural language pose significant challenges in developing an interpreter that can accurately understand the programming logic and execute instructions written in natural language.

Large Language Model Agent for Fake News Detection

no code implementations30 Apr 2024 Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

This adaptability enables FactAgent's application to news verification across various domains.

Decision Making Fake News Detection +3

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis

1 code implementation23 Apr 2024 Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang

This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time.

Decision Making Language Modelling

Memory Sharing for Large Language Model based Agents

1 code implementation15 Apr 2024 Hang Gao, Yongfeng Zhang

The adaptation of Large Language Model (LLM)-based agents to execute tasks via natural language prompts represents a significant advancement, notably eliminating the need for explicit retraining or fine tuning, but are constrained by the comprehensiveness and diversity of the provided examples, leading to outputs that often diverge significantly from expected results, especially when it comes to the open-ended questions.

Common Sense Reasoning Diversity +4

Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?

1 code implementation10 Apr 2024 Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang

In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers.

ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction

1 code implementation30 Mar 2024 Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, Yongfeng Zhang

Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts.

IDGenRec: LLM-RecSys Alignment with Textual ID Learning

1 code implementation27 Mar 2024 Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, Yongfeng Zhang

The results show that the zero-shot performance of the pre-trained foundation model is comparable to or even better than some traditional recommendation models based on supervised training, showing the potential of the IDGen paradigm serving as the foundation model for generative recommendation.

Sequential Recommendation Text Generation

AIOS: LLM Agent Operating System

1 code implementation25 Mar 2024 Kai Mei, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, Yongfeng Zhang

Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI.

Language Modelling Large Language Model +1

Large Language Models in Biomedical and Health Informatics: A Bibliometric Review

no code implementations24 Mar 2024 Huizi Yu, Lizhou Fan, Lingyao Li, Jiayan Zhou, Zihui Ma, Lu Xian, Wenyue Hua, Sijia He, Mingyu Jin, Yongfeng Zhang, Ashvin Gandhi, Xin Ma

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.

Management Medical Diagnosis

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

no code implementations12 Mar 2024 Dong Shu, Tianle Chen, Mingyu Jin, Chong Zhang, Mengnan Du, Yongfeng Zhang

We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs to enhance multi-hop link prediction in KGs.

In-Context Learning Knowledge Graphs +2

What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents

no code implementations20 Feb 2024 Mingyu Jin, Beichen Wang, Zhaoqian Xue, Suiyuan Zhu, Wenyue Hua, Hua Tang, Kai Mei, Mengnan Du, Yongfeng Zhang

In this study, we introduce "CosmoAgent," an innovative artificial intelligence framework utilizing Large Language Models (LLMs) to simulate complex interactions between human and extraterrestrial civilizations, with a special emphasis on Stephen Hawking's cautionary advice about not sending radio signals haphazardly into the universe.

Decision Making

EmojiCrypt: Prompt Encryption for Secure Communication with Large Language Models

2 code implementations8 Feb 2024 Guo Lin, Wenyue Hua, Yongfeng Zhang

While these models offer substantial benefits in terms of accessibility and functionality, they also introduce significant privacy concerns: the transmission and storage of user data in cloud infrastructures pose substantial risks of data breaches and unauthorized access to sensitive information; even if the transmission and storage of data is encrypted, the LLM service provider itself still knows the real contents of the data, preventing individuals or entities from confidently using such LLM services.

Sentiment Analysis

TrustAgent: Towards Safe and Trustworthy LLM-based Agents through Agent Constitution

1 code implementation2 Feb 2024 Wenyue Hua, Xianjun Yang, Zelong Li, Wei Cheng, Yongfeng Zhang

This paper presents an Agent-Constitution-based agent framework, TrustAgent, an initial investigation into improving the safety dimension of trustworthiness in LLM-based agents.

Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents

1 code implementation1 Feb 2024 Zelong Li, Wenyue Hua, Hao Wang, He Zhu, Yongfeng Zhang

A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable.

PAP-REC: Personalized Automatic Prompt for Recommendation Language Model

1 code implementation1 Feb 2024 Zelong Li, Jianchao Ji, Yingqiang Ge, Wenyue Hua, Yongfeng Zhang

In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts.

Language Modelling

Health-LLM: Personalized Retrieval-Augmented Disease Prediction System

no code implementations1 Feb 2024 Mingyu Jin, Qinkai Yu, Dong Shu, Chong Zhang, Lizhou Fan, Wenyue Hua, Suiyuan Zhu, Yanda Meng, Zhenting Wang, Mengnan Du, Yongfeng Zhang

Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction.

Disease Prediction Language Modelling +3

Neural Locality Sensitive Hashing for Entity Blocking

no code implementations31 Jan 2024 Runhui Wang, Luyang Kong, Yefan Tao, Andrew Borthwick, Davor Golac, Henrik Johnson, Shadie Hijazi, Dong Deng, Yongfeng Zhang

We assess the effectiveness of this approach within the context of the entity resolution problem, which frequently involves the use of task-specific metrics in real-world applications.


AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models

no code implementations17 Jan 2024 Dong Shu, Mingyu Jin, Suiyuan Zhu, Beichen Wang, ZiHao Zhou, Chong Zhang, Yongfeng Zhang

In our research, we pioneer a novel approach to evaluate the effectiveness of jailbreak attacks on Large Language Models (LLMs), such as GPT-4 and LLaMa2, diverging from traditional robustness-focused binary evaluations.

The Impact of Reasoning Step Length on Large Language Models

2 code implementations10 Jan 2024 Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du

Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.

NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

1 code implementation22 Dec 2023 Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, Yongfeng Zhang

Complex reasoning ability is one of the most important features of current LLMs, which has also been leveraged to play an integral role in complex decision-making tasks.

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

1 code implementation6 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).

AI Agent Language Modelling

Exploring Fine-tuning ChatGPT for News Recommendation

no code implementations10 Nov 2023 Xinyi Li, Yongfeng Zhang, Edward C Malthouse

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information.

News Recommendation Recommendation Systems

FMMRec: Fairness-aware Multimodal Recommendation

no code implementations26 Oct 2023 Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao, Fajie Yuan, Yongfeng Zhang

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations.

Attribute counterfactual +3

LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation

1 code implementation26 Oct 2023 Kai Mei, Yongfeng Zhang

LightLM tackles the issue by introducing a light-weight deep and narrow Transformer architecture, which is specifically tailored for direct generation of recommendation items.

Hallucination Language Modelling

A Content-Driven Micro-Video Recommendation Dataset at Scale

1 code implementation27 Sep 2023 Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, Fajie Yuan

Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.

Benchmarking Recommendation Systems +1

Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

no code implementations3 Sep 2023 Lei LI, Yongfeng Zhang, Dugang Liu, Li Chen

Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e. g., recommender systems (RS).

Recommendation Systems Re-Ranking

Language is All a Graph Needs

1 code implementation14 Aug 2023 Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, Yongfeng Zhang

The emergence of large-scale pre-trained language models has revolutionized various AI research domains.

Graph Learning Language Modelling

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

OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

4 code implementations19 Jun 2023 Shuyuan Xu, Wenyue Hua, Yongfeng Zhang

In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers.

Benchmarking Decoder +2

A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

1 code implementation19 Jun 2023 Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Considering the growing reliance on ChatGPT for language tasks, the importance of news recommendation in addressing social issues, and the trend of using language models in recommendations, this study conducts an initial investigation of ChatGPT's performance in news recommendations, focusing on three perspectives: personalized news recommendation, news provider fairness, and fake news detection.

Fairness Fake News Detection +2

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

Fairness of ChatGPT

1 code implementation22 May 2023 Yunqi Li, Lanjing Zhang, Yongfeng Zhang

This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible AI systems.


UP5: Unbiased Foundation Model for Fairness-aware Recommendation

1 code implementation20 May 2023 Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang

In this paper, we dive into the extent of unfairness exhibited by LLM-based recommender models based on both T5 and LLaMA backbones, and discuss appropriate methods for promoting equitable treatment of users in LLM-based recommendation models.

Decision Making Fairness +1

How to Index Item IDs for Recommendation Foundation Models

4 code implementations11 May 2023 Wenyue Hua, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

To emphasize the importance of item indexing, we first discuss the issues of several trivial item indexing methods, such as random indexing, title indexing, and independent indexing.

Language Modelling

Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT

no code implementations8 May 2023 Guo Lin, Yongfeng Zhang

This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs).

The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples

no code implementations30 Apr 2023 Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei

By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model.

counterfactual Counterfactual Explanation +4

PBNR: Prompt-based News Recommender System

no code implementations16 Apr 2023 Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Some previous works have used language model techniques, such as the attention mechanism, to capture users' interests based on their past behaviors, and to understand the content of articles.

Language Modelling Large Language Model +3

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

2 code implementations 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

HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention

1 code implementation6 Mar 2023 Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang

The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding.

cross-modal alignment

Exploration and Regularization of the Latent Action Space in Recommendation

1 code implementation7 Feb 2023 Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang

To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.

Recommendation Systems

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

Transferable Fairness for Cold-Start Recommendation

no code implementations25 Jan 2023 Yunqi Li, Dingxian Wang, Hanxiong Chen, Yongfeng Zhang

The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.

counterfactual Fairness +1

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

Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing

no code implementations8 Nov 2022 Wenyue Hua, Lifeng Jin, Linfeng Song, Haitao Mi, Yongfeng Zhang, Dong Yu

Pretrained natural language processing (NLP) models have achieved high overall performance, but they still make systematic errors.

Causal Structure Learning with Recommendation System

no code implementations19 Oct 2022 Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang

Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.

Decision Making Recommendation Systems

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)

1 code implementation23 Aug 2022 Hanxiong Chen, Yunqi Li, He Zhu, Yongfeng Zhang

Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.

Neural Architecture Search

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

Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems

no code implementations17 Aug 2022 Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah

Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.

Explainable Recommendation Explanation Generation +3

GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations

no code implementations4 Aug 2022 Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Zhenhua Huang, Hongshik Ahn, Gabriele Tolomei

Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.

counterfactual Graph Classification +1

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

AutoLossGen: Automatic Loss Function Generation for Recommender Systems

1 code implementation27 Apr 2022 Zelong Li, Jianchao Ji, Yingqiang Ge, Yongfeng Zhang

One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search.

Recommendation Systems

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 +1

Personalized Prompt Learning for Explainable Recommendation

1 code implementation15 Feb 2022 Lei LI, Yongfeng Zhang, Li Chen

In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages.

Explainable Recommendation Recommendation Systems +1

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

no code implementations14 Feb 2022 Xu Chen, Yongfeng Zhang, Ji-Rong Wen

Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them.

Explainable Recommendation Persuasiveness +1

Neural Logic Analogy Learning

no code implementations4 Feb 2022 Yujia Fan, Yongfeng Zhang

The analogy learning problem then becomes a True/False evaluation problem of the logical expressions.

Logical Reasoning

RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems

1 code implementation12 Jan 2022 Zohreh Ovaisi, Shelby Heinecke, Jia Li, Yongfeng Zhang, Elena Zheleva, Caiming Xiong

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.

Recommendation Systems

Graph Collaborative Reasoning

no code implementations27 Dec 2021 Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.

Link Prediction Logical Reasoning +2

From Kepler to Newton: Explainable AI for Science

no code implementations24 Nov 2021 Zelong Li, Jianchao Ji, Yongfeng Zhang

We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research.

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

CLIP-Adapter: Better Vision-Language Models with Feature Adapters

3 code implementations9 Oct 2021 Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, Yu Qiao

Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.

Prompt Engineering Representation Learning

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

Problem Learning: Towards the Free Will of Machines

no code implementations1 Sep 2021 Yongfeng Zhang

However, automatically discovering research or application problems for an area is beneficial since it helps to identify valid and potentially important problems hidden in data that are unknown to domain experts, expand the scope of tasks that we can do in an area, and even inspire completely new findings.


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

Personalized Transformer for Explainable Recommendation

1 code implementation ACL 2021 Lei LI, Yongfeng Zhang, Li Chen

Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words.

Explainable Recommendation Language Modelling +1

Personalized Counterfactual Fairness in Recommendation

1 code implementation20 May 2021 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.

counterfactual Decision Making +2

User-oriented Fairness in Recommendation

1 code implementation21 Apr 2021 Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang

To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.

Fairness Recommendation Systems +1

Efficient Non-Sampling Knowledge Graph Embedding

1 code implementation21 Apr 2021 Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, Yongfeng Zhang

Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.

Knowledge Graph Embedding

Faithfully Explainable Recommendation via Neural Logic Reasoning

1 code implementation NAACL 2021 Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.

Decision Making Explainable Recommendation +3

Variation Control and Evaluation for Generative SlateRecommendations

no code implementations26 Feb 2021 Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, Yongfeng Zhang

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations.

Recommendation Systems

EXTRA: Explanation Ranking Datasets for Explainable Recommendation

1 code implementation20 Feb 2021 Lei LI, Yongfeng Zhang, Li Chen

To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics.

Explainable Models Explainable Recommendation +1

Causal Collaborative Filtering

1 code implementation3 Feb 2021 Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang

However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance.

Collaborative Filtering counterfactual +1

On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

2 code implementations1 Feb 2021 Lei LI, Yongfeng Zhang, Li Chen

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).

Learning-To-Rank Recommendation Systems

RomeBERT: Robust Training of Multi-Exit BERT

1 code implementation24 Jan 2021 Shijie Geng, Peng Gao, Zuohui Fu, Yongfeng Zhang

In this paper, we leverage gradient regularized self-distillation for RObust training of Multi-Exit BERT (RomeBERT), which can effectively solve the performance imbalance problem between early and late exits.

Natural Language Understanding

Discrete Knowledge Graph Embedding based on Discrete Optimization

no code implementations13 Jan 2021 Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.

Knowledge Graph Embedding

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Generate Natural Language Explanations for Recommendation

no code implementations9 Jan 2021 Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang

Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.

Denoising Explainable Recommendation +4

A Representation Learning Approach to Animal Biodiversity Conservation

no code implementations COLING 2020 Meet Mukadam, Mandhara Jayaram, Yongfeng Zhang

This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings.

Representation Learning

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

1 code implementation29 Oct 2020 Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang

User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.

Explainable Recommendation Knowledge Graphs +1

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce

no code implementations21 Aug 2020 Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo

In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.

Knowledge Graphs

Neural Logic Reasoning

3 code implementations20 Aug 2020 Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang

Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.

Logical Reasoning Recommendation Systems

E-commerce Recommendation with Weighted Expected Utility

no code implementations19 Aug 2020 Zhichao Xu, Yi Han, Yongfeng Zhang, Qingyao Ai

In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns.

Collaborative Filtering Recommendation Systems

Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

no code implementations26 Jul 2020 Yikun Xian, Zuohui Fu, Qiaoying Huang, S. Muthukrishnan, Yongfeng Zhang

Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen.

Explainable Recommendation Knowledge Graphs +1

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

no code implementations8 Jul 2020 Shijie Geng, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li, Anoop Cherian

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content.

Answer Generation Graph Representation Learning

Learning Personalized Risk Preferences for Recommendation

1 code implementation6 Jul 2020 Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang

Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.

Recommendation Systems

Understanding Echo Chambers in E-commerce Recommender Systems

1 code implementation6 Jul 2020 Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang

Current research on recommender systems mostly focuses on matching users with proper items based on user interests.

Recommendation Systems

Learning Post-Hoc Causal Explanations for Recommendation

no code implementations30 Jun 2020 Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.

counterfactual Sequential Recommendation

Neural Collaborative Reasoning

3 code implementations16 May 2020 Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang

Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.

Collaborative Filtering Decision Making +3

HID: Hierarchical Multiscale Representation Learning for Information Diffusion

2 code implementations19 Apr 2020 Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia

In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.

Representation Learning

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

1 code implementation3 Feb 2020 Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen

We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Representation Learning

Neural Logic Networks

no code implementations17 Oct 2019 Shaoyun Shi, Hanxiong Chen, Min Zhang, Yongfeng Zhang

The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.

Collaborative Filtering Logical Reasoning

Explainable Product Search with a Dynamic Relation Embedding Model

no code implementations16 Sep 2019 Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft

Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.

Relation Retrieval

Conversational Product Search Based on Negative Feedback

no code implementations4 Sep 2019 Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft

So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.

Conversational Search

Attentive History Selection for Conversational Question Answering

2 code implementations26 Aug 2019 Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer

First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.

Conversational Question Answering Conversational Search +2

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

1 code implementation12 Jun 2019 Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang

To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.

Causal Inference Decision Making +3

BERT with History Answer Embedding for Conversational Question Answering

1 code implementation14 May 2019 Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, Mohit Iyyer

One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.

Conversational Question Answering Conversational Search +2

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

2 code implementations29 Apr 2019 Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose

In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.

Collaborative Filtering Recommendation Systems +1

Personalized Re-ranking for Recommendation

1 code implementation15 Apr 2019 Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.

Recommendation Systems Re-Ranking

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning +1

User Intent Prediction in Information-seeking Conversations

1 code implementation11 Jan 2019 Chen Qu, Liu Yang, Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu

Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.

Conversational Search Feature Engineering +1

Attentive Aspect Modeling for Review-aware Recommendation

no code implementations11 Nov 2018 Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua

The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product.


Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

5 code implementations9 May 2018 Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang

Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items.

Collaborative Filtering Explainable Recommendation +3

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

1 code implementation1 May 2018 Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Knowledge Distillation Retrieval +1

Explainable Recommendation: A Survey and New Perspectives

no code implementations30 Apr 2018 Yongfeng Zhang, Xu Chen

In this survey, we provide a comprehensive review for the explainable recommendation research.

Explainable Recommendation Persuasiveness +2

Analyzing and Characterizing User Intent in Information-seeking Conversations

no code implementations23 Apr 2018 Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.

Conversational Search Question Answering

Visually Explainable Recommendation

no code implementations31 Jan 2018 Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha

By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.

Explainable Recommendation Recommendation Systems

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

2 code implementations CIKM 2017 Yongfeng Zhang, Qingyao Ai, Xu Chen, W. Bruce Croft

In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures.

Context Aware Product Recommendation Learning-To-Rank +2

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

no code implementations11 Feb 2015 Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma

In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.

Classification General Classification +2

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