no code implementations • ACL 2022 • Shijie Geng, Zuohui Fu, Yingqiang Ge, Lei LI, Gerard de Melo, Yongfeng Zhang
In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items.
no code implementations • 31 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.
no code implementations • 28 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.
3 code implementations • 11 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.
no code implementations • 30 Apr 2024 • Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
This adaptability enables FactAgent's application to news verification across various domains.
1 code implementation • 23 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.
1 code implementation • 15 Apr 2024 • Hang Gao, Yongfeng Zhang
In the realm of artificial intelligence, 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 for fixed-answer tasks such as common sense questions and yes/no queries.
1 code implementation • 10 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.
1 code implementation • 30 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.
1 code implementation • 27 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.
2 code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 12 Mar 2024 • Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Chong Zhang, Mengnan Du, Yongfeng Zhang
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.
1 code implementation • 4 Mar 2024 • Lizhou Fan, Wenyue Hua, Xiang Li, Kaijie Zhu, Mingyu Jin, Lingyao Li, Haoyang Ling, Jinkui Chi, Jindong Wang, Xin Ma, Yongfeng Zhang
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
no code implementations • 20 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.
no code implementations • 16 Feb 2024 • Mingyu Jin, Hua Tang, Chong Zhang, Qinkai Yu, Chengzhi Liu, Suiyuan Zhu, Yongfeng Zhang, Mengnan Du
However, there is a research gap in the LLMs' preferences in this field.
2 code implementations • 8 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.
1 code implementation • 2 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.
1 code implementation • 1 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.
1 code implementation • 1 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.
1 code implementation • 1 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.
no code implementations • 31 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.
no code implementations • 17 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.
1 code implementation • 10 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.
1 code implementation • 22 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.
1 code implementation • 6 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).
1 code implementation • 28 Nov 2023 • Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, Yongfeng Zhang
Can we avoid wars at the crossroads of history?
no code implementations • 10 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.
1 code implementation • 26 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.
no code implementations • 26 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.
1 code implementation • 27 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.
no code implementations • 3 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).
1 code implementation • 14 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.
1 code implementation • 2 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.
1 code implementation • 2 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.
no code implementations • 30 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.
1 code implementation • 19 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.
4 code implementations • 19 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.
1 code implementation • 23 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.
1 code implementation • 22 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.
1 code implementation • 20 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.
4 code implementations • 11 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.
no code implementations • 8 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).
no code implementations • 30 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.
no code implementations • 16 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.
no code implementations • 11 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.
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).
1 code implementation • 6 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.
1 code implementation • 7 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.
1 code implementation • 27 Jan 2023 • Juntao Tan, Yongfeng Zhang
This paper presents ExplainableFold, an explainable AI framework for protein structure prediction.
no code implementations • 25 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.
no code implementations • 8 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.
no code implementations • 8 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.
no code implementations • 19 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.
1 code implementation • 23 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.
1 code implementation • 23 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.
no code implementations • 17 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.
no code implementations • 4 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.
no code implementations • 25 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.
no code implementations • 26 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.
1 code implementation • 27 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.
no code implementations • 24 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.
2 code implementations • 24 Mar 2022 • Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives.
1 code implementation • 17 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.
1 code implementation • 15 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.
no code implementations • 14 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.
no code implementations • 4 Feb 2022 • Yujia Fan, Yongfeng Zhang
The analogy learning problem then becomes a True/False evaluation problem of the logical expressions.
1 code implementation • 12 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.
no code implementations • 1 Jan 2022 • Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, Yongfeng Zhang
One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness.
no code implementations • 27 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.
no code implementations • 24 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.
1 code implementation • 14 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.
2 code implementations • 9 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.
no code implementations • 5 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.
no code implementations • 1 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.
2 code implementations • 24 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.
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.
1 code implementation • 20 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.
1 code implementation • 21 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.
1 code implementation • 21 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.
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.
no code implementations • 26 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.
1 code implementation • 20 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.
1 code implementation • 3 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.
2 code implementations • 1 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).
1 code implementation • 24 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.
no code implementations • 13 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.
1 code implementation • 10 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.
no code implementations • 9 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.
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.
1 code implementation • 29 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.
no code implementations • 21 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.
3 code implementations • 20 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.
no code implementations • 19 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.
no code implementations • 26 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.
no code implementations • 8 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.
1 code implementation • 6 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.
1 code implementation • 6 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.
no code implementations • 30 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.
no code implementations • 3 Jun 2020 • Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
3 code implementations • 16 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.
2 code implementations • 19 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.
1 code implementation • 3 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.
no code implementations • 17 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.
no code implementations • 16 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.
no code implementations • 4 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.
2 code implementations • 26 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.
1 code implementation • 12 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.
1 code implementation • 14 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.
2 code implementations • 29 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.
1 code implementation • 15 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.
no code implementations • 3 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.
no code implementations • 11 Jan 2019 • Chen Qu, Liu Yang, Bruce Croft, Falk Scholer, Yongfeng Zhang
Information retrieval systems are evolving from document retrieval to answer retrieval.
1 code implementation • 11 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.
no code implementations • 11 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.
5 code implementations • 9 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.
Ranked #3 on Link Prediction on Yelp
1 code implementation • 1 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.
no code implementations • 30 Apr 2018 • Yongfeng Zhang, Xu Chen
In this survey, we provide a comprehensive review for the explainable recommendation research.
no code implementations • 23 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.
1 code implementation • 17 Mar 2018 • Yongfeng Zhang, Qingyao Ai, Xu Chen, Pengfei Wang
In this work, we propose to reason over knowledge base embeddings for personalized recommendation.
no code implementations • 31 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.
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.
no code implementations • 17 Jul 2017 • Liu Yang, Hamed Zamani, Yongfeng Zhang, Jiafeng Guo, W. Bruce Croft
We further evaluate the neural matching models in the next question prediction task in conversations.
no code implementations • 11 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.