Large Language Models (LLMs) are powerful AI systems trained on vast text data, excelling at tasks like text generation and comprehension. LLM-based AI agents, powered by these models, enable complex task-solving, advanced and context-aware conversations with users, transforming industries from research and development to customer support to content creation. They represent a pivotal development in AI technology, with far-reaching implications for human-computer interaction. Our research on LLM and LLM-based AI Agents include both single-agent research and multi-agent research, focusing on the development of both algorithms and systems of LLM and Agents.
Related Publications:
Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li and Yongfeng Zhang. OpenAGI: When LLM Meets Domain Experts. In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), December 10 - December 16, New Orleans, Louisiana, US. [PDF]
Yingqiang Ge, Yujie Ren, Wenyue Hua, Shuyuan Xu, Juntao Tan, Yongfeng Zhang. LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem. [PDF]
Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, Yongfeng Zhang. War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars. [PDF]
Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, Yongfeng Zhang, Libby Hemphill. NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes. [PDF]
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu and Yongfeng Zhang. Natural Language is All a Graph Needs. [PDF]
LLM and Foundation Models for Search and Recommendation
Foundation Models such as Large Languge Models not only improves the search and recommendation performance with its ability of learning complex relations, but also makes it possible to unify various traditional or innovative search and recommendation tasks under a single framwork, making it possible for the community to consistently improve the one model together which benefits a lot of downstream tasks. Most importantly, the emergent ability of foundation models brings capabilities that are otherwise unseen in training tasks and traditional neural network models. This project aims to develop, improve, explore, and understand the capabilities and implications of foundation models for search and recommendation.
Related Publications:
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge and Yongfeng Zhang. Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys 2022), September 18 - 23, 2022, Seattle, WA, USA. [PDF][Slides]
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang. How to Index Item IDs for Recommendation Foundation Models. In Proceedings of 1st International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR-AP 2023), November 26 - 29, 2023, Beijing, China. [PDF]
Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, Yongfeng Zhang. VIP5: Towards Multimodal Foundation Models for Recommendation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), December 6 - December 10, 2023, Singapore. [PDF]
Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang. UP5: Unbiased Foundation Model for Fairness-aware Recommendation. [PDF]
Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, Yongfeng Zhang. GenRec: Large Language Model for Generative Recommendation. In Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024), March 24 - 28, 2024, Glasgow, Scotland. [PDF]
Xinyi Li, Yongfeng Zhang, Edward C. Malthouse. Prompt-based Generative News Recommendation (PGNR): Accuracy and Controllability. In Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024), March 24 - 28, 2024, Glasgow, Scotland. [PDF]
Shuyuan Xu, Wenyue Hua and Yongfeng Zhang. OpenP5: Benchmarking Foundation Models for Recommendation. [PDF]
Guo Lin and Yongfeng Zhang. Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT. [PDF]
Lei Li, Yongfeng Zhang and Li Chen. Prompt Distillation for Efficient LLM-based Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), October 21 - 25, 2023, Birmingham, UK. [PDF]
Lei Li, Yongfeng Zhang and Li Chen. Personalized Transformer for Explainable Recommendation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL 2021), August 1 - 6, 2021, Bangkok, Thailand. [PDF]
Lei Li, Yongfeng Zhang, Li Chen. Personalized Prompt Learning for Explainable Recommendation. In ACM Transactions on Information Systems (TOIS). [PDF]
Shijie Geng, Zuohui Fu, Yingqiang Ge, Lei Li, Gerard de Melo and Yongfeng Zhang. Improving Personalized Explanation Generation through Visualization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), May 22 - 27, 2022, Dublin, Ireland. [PDF]
Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard de Melo and Yongfeng Zhang. Path Language Modeling over Knowledge Graphs for Explainable Recommendation. In Proceedings of the Web Conference 2022 (WWW 2022), April 25 - 29, 2022, Lyon, France. [PDF]
Explainable AI for Science
AI has been playing an indispensable role in various scientific discovery tasks in physics, chemistry, biology, materials science, medical science, just to name a few. However, even though AI has achieved many success in making accurate predictions or discoveries in these scientific reseach tasks, the blackbox nature of many AI/deep learning models makes it difficult for scientists to gain deep insights from the predictions made by AI. Understanding WHY the AI models make certain predictions is extrememly important in scientific research, because science is not only about the know how, but also (or even more) about the know why. The long-term vision of this project is to highlight the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future.
Related Publications:
Zelong Li, Jianchao Ji, Yongfeng Zhang. From Kepler to Newton: Explainable AI for Science. ICML-AI4Science 2022. [PDF][Video and Slides]
Juntao Tan and Yongfeng Zhang. ExplainableFold: Understanding AlphaFold Prediction with Explainable AI. In Proceedings of the 29th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), August 6 - 10, 2023, Long Beach, California, United States. [PDF]
Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li and Yongfeng Zhang. Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning. In Proceedings of the Web Conference 2022 (WWW 2022), April 25 - 29, 2022, Lyon, France. [PDF]
Meet Mukadam, Mandhara Jayaram and Yongfeng Zhang. A Representation Learning Approach to Animal Biodiversity Conservation. In Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), December 8 - 13, 2020, Online, Barcelona. [PDF]
Machine Reasoning, Neural-Symbolic Learning and Cognitive Intelligence
Recent years have witnessed the success of machine learning and especially deep learning in many research areas such as IR, Data Mining, Vision and Natural Language Processing. Although various learning approaches have demonstrated satisfying performance in perceptual tasks such as pattern recognition and matching by extracting useful features from data, the area still needs a large amount of research to advance from perceptual learning to cognitive reasoning in the coming years towards cognitive intelligence. This includes but is not limited to neural logic reasoning, neural-symbolic reasoning, causal reasoning, knowledge reasoning, commonsense reasoning and brain-inspired cognitive reasoning models.
Related Publications:
Hanxiong Chen, Shaoyun Shi, Yunqi Li and Yongfeng Zhang. Neural Collaborative Reasoning. In Proceedings of the Web Conference 2021 (WWW 2021), April 19 - 23, 2021, Ljubljana, Slovenia. [PDF]
Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang and Yongfeng Zhang. Neural Logic Reasoning. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), October 19 - 23, 2020, Virtual Event, Ireland. [PDF]
Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu and Yongfeng Zhang. Graph Collaborative Reasoning. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022), February 21 - 25, 2022, Phoenix, Arizona. [PDF]
Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang. Counterfactual Collaborative Reasoning. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM 2023), February 27 - March 3, 2023, Singapore. [PDF]
Wenyue Hua and Yongfeng Zhang. System 1 + System 2 = Better World: Neural-Symbolic Chain of Logic Reasoning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), December 7 – 11, 2022, Abu Dhabi. [PDF]
Hanxiong Chen, Yunqi Li, He Zhu and Yongfeng Zhang. Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS). In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), October 17 - 21, 2022, Hybrid Conference, Hosted in Atlanta, Georgia, USA. [PDF]
Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang. CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), October 19 - 23, 2020, Virtual Event, Ireland. [PDF]
Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard de Melo and Yongfeng Zhang. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), July 21 - 25, 2019, Paris, France. [PDF]
Zuohui Fu, Yikun Xian, Yaxin Zhu, Shuyuan Xu, Zelong Li, Gerard de Melo and Yongfeng Zhang. HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo and Yongfeng Zhang. Faithfully Explainable Recommendation via Neural Logic Reasoning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021), June 6 - 11, 2021, Mexico City, Mexico. [PDF]
Causal Machine Learning
Researchers in the AI community have realized the importance of advancing from correlative learning to causal learning, which aims to address a wide range of AI problems in machine learning, machine reasoning, information retrieval, recommender systems, computer vision, and natural language processing. Notable techniques that we are interested in include but are not limited to Causal Explainable AI, Causal Unbaised AI, Causal Fairness, Causal Robustness, and Multi-Modality Causal Learning.
Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke and Yongfeng Zhang. Dynamic Causal Collaborative Filtering. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), October 17 - 21, 2022, Hybrid Conference, Hosted in Atlanta, Georgia, USA. [PDF]
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen and Yongfeng Zhang. Counterfactual Explainable Recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), November 1 - 5, 2021, Gold Coast, Australia. [PDF]
Shuyuan Xu, Jianchao Ji, Yunqi Li, Yingqiang Ge, Juntao Tan, Yongfeng Zhang. Causal Inference for Recommendation: Foundations, Methods and Applications. arXiv:2301.04016. [PDF]
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge and Yongfeng Zhang. Towards Personalized Fairness based on Causal Notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Yunqi Li, Hanxiong Chen, Juntao Tan and Yongfeng Zhang. Causal Factorization Machine for Robust Recommendation. In Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL 2022), June 20 - 24, 2022, Cologne, Germany and Online. [PDF]
Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao and Ji-Rong Wen. Counterfactual Data-Augmented Sequential Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Kun Xiong, Wenwen Ye, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, Binbin Hu, Zhiqiang Zhang and Jun Zhou. Counterfactual Review-based Recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), November 1 - 5, 2021, Gold Coast, Australia. [PDF]
Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, Yongfeng Zhang. Learning Causal Explanations for Recommendation. In Proceedings of the 1st International Workshop on Causality in Search and Recommendation, July 15, 2021, Virtual Event, Canada. [PDF]
Fairness in AI and Machine Learning
Machine Learning is widely applied in various systems that post direct or indirect influences on our human society, such as recommender systems, search engines, adversiting systems, and social networks. Without careful design and treatment, such algorithms may learn implicit bias from data and produce unfair decisions to human users. This project aims to improve the fairness of various intelligent systems towards long-term, human-friendly and sustainable development on the intelligent systems.
Related Publications:
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge and Yongfeng Zhang. User-oriented Fairness in Recommendation. In Proceedings of the Web Conference 2021 (WWW 2021), April 19 - 23, 2021, Ljubljana, Slovenia. [PDF]
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge and Yongfeng Zhang. Towards Personalized Fairness based on Causal Notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang. Towards Long-term Fairness in Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021), March 8 - 12, 2021, Virtual Event, Israel. [PDF]
Shuchang Liu, Yingqiang Ge, Shuyuan Xu, Yongfeng Zhang and Amelie Marian. Fairness-aware Federated Matrix Factorization. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys 2022), September 18 - 23, 2022, Seattle, WA, USA. [PDF][Slides]
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li and Yongfeng Zhang. Explainable Fairness in Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), July 11 - 15, 2022, Madrid, Spain. [PDF]
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu and Yongfeng Zhang. Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022), February 21 - 25, 2022, Phoenix, Arizona. [PDF]
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu and Yongfeng Zhang. Fairness in Recommendation: A Survey. arXiv:2205.13619. [PDF]
Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian and Yongfeng Zhang. A Survey on Trustworthy Recommender Systems. arXiv:2207.12515. [PDF]
Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang and Gerard de Melo. Fairness-aware Explainable Recommendation over Knowledge Graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), July 25 - 30, 2020, Virtual Event, China. [PDF]
Guang Wang, Yongfeng Zhang, Zhihan Fang, Shuai Wang, Fan Zhang, Desheng Zhang. FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets. In Proceedings of the ACM Conference on Interactive, Mobile, Wearable and Ubiquitous Technologies (UBICOMP 2020). [PDF]
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma. Fairness-Aware Group Recommendation with Pareto Efficiency. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017), August 27 - 31, 2017, Como, Italy. [PDF]
Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou and Yongfeng Zhang. Understanding Echo Chambers in E-commerce Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), July 25 - 30, 2020, Virtual Event, China. [PDF]
Explainable AI
Explainable AI aims to not only produce good predictions or decisions but also explain the predictions or decisions for humans to better understand and refine the model. Explainable Recommendation refers to the (personalized) recommendation or decisiono making systems that not only provide the user with the good recommendations or advices, but also let the users know why such advices are provided, i.e., they try to address the problem of "why" in decision making systems. Explainable recommendation algorithms devise interpretable models and generates intuitive explanations for users, which help to improve the effectiveness, efficiency, persuasiveness, user satisfaction and trust of the decision making systems.
Related Publications:
Yongfeng Zhang and Xu Chen. Explainable Recommendation: A Survey and New Perspectives. arXiv Preprint 2018. arXiv:1804.11192. [PDF]
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu and Shaoping Ma. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. In Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2014), July 6 - 11, 2014, Gold Coast, Australia. [PDF]
Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian and Yongfeng Zhang. A Survey on Trustworthy Recommender Systems. arXiv:2207.12515. [PDF]
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen and Yongfeng Zhang. Counterfactual Explainable Recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), November 1 - 5, 2021, Gold Coast, Australia. [PDF]
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li and Yongfeng Zhang. Explainable Fairness in Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), July 11 - 15, 2022, Madrid, Spain. [PDF]
Xu Chen, Yongfeng Zhang, Zheng Qin. Dynamic Explainable Recommendation based on Neural Attentive Models. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), January 27 – February 1, 2019, Hawaii, USA. [PDF]
Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Algorithms. 2018, 11(9). Special Issue Collaborative Filtering and Recommender Systems. [PDF]
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. Learning to Rank Features for Recommendation over Multiple Categories. In Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2016), July 17 - 21, 2016, Pisa, Italy. [PDF]
Xu Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Hongyuan Zha, Zheng Qin and Jiaxi Tang. Sequential Recommendation with User Memory Networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018), February 5 - 9, 2018, Los Angeles, California, USA. [PDF]
Yongfeng Zhang. Explainable Recommendation: Theory and Applications. (PhD Thesis). arXiv Preprint 2017. arXiv:1708.06409. [PDF]
Yongfeng Zhang. Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation. In Proceedings of the 8th International Conference on Web Search and Data Mining (WSDM 2015), Feb. 2 - 6, 2015, Shanghai, China. [PDF]
Yongfeng Zhang. Browser-Oriented Universal Cross-Site Recommendation and Explanation based on User Browsing Logs. In Proceedings of the 8th ACM Conference Series on Recommender Systems (RecSys 2014), Oct. 6 - 10, 2014, Foster City, Silicon Valley, USA. [PDF]
Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma. Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis. In Proceedings of the 24th International World Wide Web Conference (WWW 2015), May 18 - 22, 2015, Florence, Italy. [PDF]
Lei Li, Yongfeng Zhang and Li Chen. Personalized Transformer for Explainable Recommendation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL 2021), August 1 - 6, 2021, Bangkok, Thailand. [PDF]
Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, Shan Muthukrishnan, Yongfeng Zhang. EX3: Explainable Attribute-aware Item-set Recommendations. In Proceedings of the 15th ACM Recommender Systems Conference (RecSys 2021), September 27 - October 1, 2021, Amsterdam, Netherland. [PDF]
Lei Li, Yongfeng Zhang and Li Chen. EXTRA: Explanation Ranking Datasets for Explainable Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo and Yongfeng Zhang. Faithfully Explainable Recommendation via Neural Logic Reasoning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021), June 6 - 11, 2021, Mexico City, Mexico. [PDF]
Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, Yongfeng Zhang. Learning Causal Explanations for Recommendation. In Proceedings of the 1st International Workshop on Causality in Search and Recommendation, July 15, 2021, Virtual Event, Canada. [PDF]
Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang. CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), October 19 - 23, 2020, Virtual Event, Ireland. [PDF]
Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard de Melo and Yongfeng Zhang. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), July 21 - 25, 2019, Paris, France. [PDF]
Lei Li, Yongfeng Zhang, Li Chen. Generate Neural Template Explanations for Recommendation. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), October 19 - 23, 2020, Virtual Event, Ireland. [PDF]
Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang and Gerard de Melo. Fairness-aware Explainable Recommendation over Knowledge Graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), July 25 - 30, 2020, Virtual Event, China. [PDF]
Lei Li, Li Chen, Yongfeng Zhang. Towards Controllable Explanation Generation for Recommender Systems via Neural Template. In Proceedings of the Web Conference 2020 (WWW 2020), April 20 - 24, 2020, Virtual Event, Taipei. [PDF]
With the continuous shifting of human activities from offline to online, the Web is no longer just a platform for information sharing, seeking, and transmission, but a huge online economy where various products or services are distributed from producers to consumers. As a result, a fundamentally important role of the Web economy is Online Resource Allocation (ORA) from producers to consumers, such as product allocation in E-commerce, job allocation in freelancing platforms, and driver resource allocation in P2P riding services. Since users have the freedom to choose, such allocations are not provided in a forced manner, but usually in forms of personalized recommendation or search. This project aims at developing machine learning methods for economic analysis of the Web economy. In particular, we develop machine learning algorithms over large-scale data to divise recommendation and resource allocation methods for the Web economy. By integrating machine learning with principled economic theories or intuitions, we can achieve targeted goals in online resource allocation, such as intelligent marketing, welfare distribution between consumers and producers, improving economic efficiency, and cost reduction in terms of price, time, location, etc.
Related Publications:
Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Shijie Geng, Zuohui Fu and Yongfeng Zhang. Maximizing Marginal Utility per Dollar for Economic Recommendation. In Proceedings of the Web Conference 2019 (WWW 2019), May 13 - 17, 2019, San Francisco, USA. [PDF]
Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou and Yongfeng Zhang. Value-aware Recommendation based on Reinforcement Profit Maximization. In Proceedings of the Web Conference 2019 (WWW 2019), May 13 - 17, 2019, San Francisco, USA. [PDF]
Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, and Shaoping Ma. Economic Recommendation with Surplus Maximization. In Proceedings of the 25th International World Wide Web Conference (WWW 2016), April 11 - 15, 2016, Montreal, Canada. [PDF]
Qi Zhao, Yongfeng Zhang, Yi Zhang, and Daniel Friedman. Multi-Product Utility Maximization for Economic Recommendation. In Proceedings of the 10th International Conference on Web Search and Data Mining (WSDM 2017), February 6 - 10, 2017, Cambridge, UK. [PDF]
Yongfeng Zhang, Yi Zhang and Daniel Friedman. Economic Recommendation based on Pareto Efficient Resource Allocation. Science Center Berlin for Social Research Discussion Papers, Wissenschaftszentrum Berlin für Sozialforschung. [PDF]
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma. Fairness-Aware Group Recommendation with Pareto Efficiency. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017), August 27 - 31, 2017, Como, Italy. [PDF]
Xiao Lin, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. Boosting Moving Average Reversion Strategy for Online Portfolio Selection: A Meta-Learning Approach. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), March 27 - 30, 2017, Suzhou, China. [PDF]
Zhichao Xu, Yi Han, Yongfeng Zhang, Qingyao Ai. E-commerce Recommendation with Weighted Expected Utility. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), October 19 - 23, 2020, Virtual Event, Ireland. [PDF]
Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun and Yongfeng Zhang. Learning Personalized Risk Preferences for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), July 25 - 30, 2020, Virtual Event, China. [PDF]
Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou and Yongfeng Zhang. Understanding Echo Chambers in E-commerce Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), July 25 - 30, 2020, Virtual Event, China. [PDF]
Conversational AI
Among the many techniques that compose an intelligent Web, a Conversational System (such as Google Now, Apple Siri, and Microsoft Cortana) is one that serves as the direct interactive portal for end-users, which is expected to revolutionize human-computer interaction in the coming years. With recent progress on IR, NLP and IoT, such systems have also been deployed as physical devices such as Google Home, Amazon Echo, and Apple HomePod, opening up more opportunities for applications in a smart home. Due to users' constant need to look for information to support both work and daily life, a Conversational Search or Recommendation system will be one of the key techniques. Conversational search and recommendation aim at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations.
Related Publications:
Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W. Bruce Croft. Towards Conversational Search and Recommendation: System Ask, User Respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), October 22 - 26, 2018, Turin, Italy. [PDF]
Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, Bruce Croft, Jun Huang, Haiqing Chen. Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems. In Proceedings of the 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), July 8 - 12, 2018, Ann Arbor, Michigan, USA. [PDF]
Chen Qu, Liu Yang, Bruce Croft, Johanne R Trippas, Yongfeng Zhang and Minghui Qiu. Analyzing and Characterizing User Intent in Information-seeking Conversations. In Proceedings of the 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), July 8 - 12, 2018, Ann Arbor, Michigan, USA. [PDF]
Liu Yang, Hamed Zamani, Yongfeng Zhang, Jiafeng Guo, and W. Bruce Croft. Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation. In Proceedings of the SIGIR 2017 Workshop on Neural Information Retrieval (NEUIR 2017), August 7 - 11, 2017, Tokyo, Japan. [PDF]
Chen Qu, Liu Yang, Bruce Croft, Yongfeng Zhang, Johanne R Trippas and Minghui Qiu. User Intent Prediction in Information-seeking Conversations. In Proceedings of the 2019 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2019), March 10 – 14, 2019, Glasgow, Scotland, UK. [PDF]
Chen Qu, Liu Yang, Bruce Croft, Falk Scholer and Yongfeng Zhang. Answer Interaction in Non-factoid Question Answering Systems. In Proceedings of the 2019 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2019), March 10 – 14, 2019, Glasgow, Scotland, UK. [PDF]
Zuohui Fu, Yikun Xian, Yaxin Zhu, Shuyuan Xu, Zelong Li, Gerard de Melo and Yongfeng Zhang. HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 11 - 15, 2021, Virtual Event, Canada. [PDF]
Product Search
Product search in e-commerce exhibits unique challenges compared with general web search. Because user preferences may be different even on the same type of products, the product search task can be highly personalized in nature. Different from document search, products are described by a hybridization of structured and unstructured data, including product description, image, user ratings/reviews, structured knowledge base, etc. Product search is also a relatively more difficult task because we not only care about user clicks but also user purchase behavoirs, and purchase behaviors cost a lot more than simply clicking a result and are thus more sparse. The product search task is also closely related to the system profits in e-commerce by prodiving satisfactory results that can attract user purchase.
Related Publications:
Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, and W. Bruce Croft. Learning a Hierarchical Embedding Model for Personalized Product Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), August 7 - 11, 2017, Tokyo, Japan. [PDF]
Deep Learning for Recommendation and Search
Our research on deep learning for recommendationo mostly focus on representation learnining for recommendation. To do so, we incoporate multimodal and heterogeneous information sources for user personalization and item recommendataion, and the tasks include top-N recommendation, sequential recommendation, multimedia recommendation, etc. Our Joint Representation Learning (JRL) model achieved 2~3 times of improvment against traditional shallow models for top-N recommendation (N=10), in terms of NDCG, Precision, Recall, and Hit-Ratio, on several product domains of a standard Amazon dataset.
Related Publications:
Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W. Bruce Croft. Towards Conversational Search and Recommendation: System Ask, User Respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), October 22 - 26, 2018, Turin, Italy. [PDF]
Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, Bruce Croft, Jun Huang, Haiqing Chen. Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems. In Proceedings of the 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), July 8 - 12, 2018, Ann Arbor, Michigan, USA. [PDF]
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. Joint Representation Learning for Top-N Recommendation with Heterogenous Information Sources. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), November 6 - 10, 2017, Singapore. [PDF]
Xu Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Hongyuan Zha, Zheng Qin and Jiaxi Tang. Sequential Recommendation with User Memory Networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018), February 5 - 9, 2018, Los Angeles, California, USA. [PDF]
Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, Zheng Qin. Personalized Key Frame Recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), August 7-11, 2017, Tokyo, Japan. [PDF]
Xu Chen, Wayne Xin Zhao, Yongfeng Zhang, Zheng Qin, Wenwen Ye. A Collaborative Neural Model for Rating Prediction by Leveraging User Reviews and Product Images. In Proceedings of the 13th Asia Information Retrieval Societies Conference (AIRS 2017), November 22 - 24, 2017, Jeju Island, Korea. Best Paper Award. [PDF]
Sentiment Analysis and Mental Health
Extracting sentimental elements from textual corpora serves as the basis for a lot of higher-level research tasks such as recommendation, search, document summarization, public opinion analysis, intelligent marketing, etc. By integrating statistical and machine learning approaches, we developed a phrase-level sentiment analysis toolkit that extracts 'feature-opinion-sentiment' triplets to construct a context-sensitive sentiment lexicon from large scale user textual reviews. For example, in the product domain of mobile phone, sampled triplets include 'picture-clear-positive', 'battery life-short-negative', 'quality-high-positive', or 'noise-high-negative'. It further contains a module that is capable of matching the triplets contained in a piece of review, and another module that contructs feature-level profiles for online items according to the user reviews made towards a specific product. For more details about the toolkit and its application, please checkout the "Sentires: Phrase-level Sentiment Analysis toolkit" under the Software of my homepage.
Related Publications:
Huijie Lin, Jia Jia, Jiezhong Qiu, Yongfeng Zhang, Guangyao Shen, Lexing Xie, Jie Tang, Ling Feng, and Tat-Seng Chua. Detecting Stress Based on Social Interactions in Social Networks. In IEEE Transactions on Knowledge and Data Engineering (TKDE), Mar 2017. [PDF]
Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu and Shaoping Ma. Do Users Rate or Review? Boost Phrase-level Sentiment Labeling with Review-level Sentiment Classification. In Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2014) (short paper), July 6 - 11, 2014, Gold Coast, Australia. [PDF][poster]
Yunzhi Tan, Yongfeng Zhang, Min Zhang, Yiqun Liu and Shaoping Ma. A Unified Framework for Emotional Elements Extraction based on Finite State Matching Machine. In Proceedings of the 2nd CCF Conference on Natural Language Processing and Chinese Computing (NLP&CC 2013), Nov. 15 - 19, 2013, Chongqing, China. [PDF]
Yongfeng Zhang, Min Zhang, Yiqun Liu and Shaoping Ma. Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification. arXiv Preprint 2015. arXiv:1502.03322. [PDF]
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu and Shaoping Ma. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. In Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2014), July 6 - 11, 2014, Gold Coast, Australia. [PDF]
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. Learning to Rank Features for Recommendation over Multiple Categories. In Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2016), July 17 - 21, 2016, Pisa, Italy. [PDF]
Yongfeng Zhang. Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation. The 8th International Conference on Web Search and Data Mining (WSDM 2015), Feb. 2 - 6, 2015, Shanghai, China. [PDF]
Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma. Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis. In Proceedings of the 24th International World Wide Web Conference (WWW 2015), May 18 - 22, 2015, Florence, Italy. [PDF]
Projects
Large Language Model and AI Agents
Large Language Models (LLMs) are powerful AI systems trained on vast text data, excelling at tasks like text generation and comprehension. LLM-based AI agents, powered by these models, enable complex task-solving, advanced and context-aware conversations with users, transforming industries from research and development to customer support to content creation. They represent a pivotal development in AI technology, with far-reaching implications for human-computer interaction. Our research on LLM and LLM-based AI Agents include both single-agent research and multi-agent research, focusing on the development of both algorithms and systems of LLM and Agents.
Related Publications:LLM and Foundation Models for Search and Recommendation
Foundation Models such as Large Languge Models not only improves the search and recommendation performance with its ability of learning complex relations, but also makes it possible to unify various traditional or innovative search and recommendation tasks under a single framwork, making it possible for the community to consistently improve the one model together which benefits a lot of downstream tasks. Most importantly, the emergent ability of foundation models brings capabilities that are otherwise unseen in training tasks and traditional neural network models. This project aims to develop, improve, explore, and understand the capabilities and implications of foundation models for search and recommendation.
Related Publications:Explainable AI for Science
AI has been playing an indispensable role in various scientific discovery tasks in physics, chemistry, biology, materials science, medical science, just to name a few. However, even though AI has achieved many success in making accurate predictions or discoveries in these scientific reseach tasks, the blackbox nature of many AI/deep learning models makes it difficult for scientists to gain deep insights from the predictions made by AI. Understanding WHY the AI models make certain predictions is extrememly important in scientific research, because science is not only about the know how, but also (or even more) about the know why. The long-term vision of this project is to highlight the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future.
Related Publications:Machine Reasoning, Neural-Symbolic Learning and Cognitive Intelligence
Recent years have witnessed the success of machine learning and especially deep learning in many research areas such as IR, Data Mining, Vision and Natural Language Processing. Although various learning approaches have demonstrated satisfying performance in perceptual tasks such as pattern recognition and matching by extracting useful features from data, the area still needs a large amount of research to advance from perceptual learning to cognitive reasoning in the coming years towards cognitive intelligence. This includes but is not limited to neural logic reasoning, neural-symbolic reasoning, causal reasoning, knowledge reasoning, commonsense reasoning and brain-inspired cognitive reasoning models.
Related Publications:Causal Machine Learning
Researchers in the AI community have realized the importance of advancing from correlative learning to causal learning, which aims to address a wide range of AI problems in machine learning, machine reasoning, information retrieval, recommender systems, computer vision, and natural language processing. Notable techniques that we are interested in include but are not limited to Causal Explainable AI, Causal Unbaised AI, Causal Fairness, Causal Robustness, and Multi-Modality Causal Learning.
Related Publications:Fairness in AI and Machine Learning
Machine Learning is widely applied in various systems that post direct or indirect influences on our human society, such as recommender systems, search engines, adversiting systems, and social networks. Without careful design and treatment, such algorithms may learn implicit bias from data and produce unfair decisions to human users. This project aims to improve the fairness of various intelligent systems towards long-term, human-friendly and sustainable development on the intelligent systems.
Related Publications:Explainable AI
Explainable AI aims to not only produce good predictions or decisions but also explain the predictions or decisions for humans to better understand and refine the model. Explainable Recommendation refers to the (personalized) recommendation or decisiono making systems that not only provide the user with the good recommendations or advices, but also let the users know why such advices are provided, i.e., they try to address the problem of "why" in decision making systems. Explainable recommendation algorithms devise interpretable models and generates intuitive explanations for users, which help to improve the effectiveness, efficiency, persuasiveness, user satisfaction and trust of the decision making systems.
Related Publications:AI and Economics
With the continuous shifting of human activities from offline to online, the Web is no longer just a platform for information sharing, seeking, and transmission, but a huge online economy where various products or services are distributed from producers to consumers. As a result, a fundamentally important role of the Web economy is Online Resource Allocation (ORA) from producers to consumers, such as product allocation in E-commerce, job allocation in freelancing platforms, and driver resource allocation in P2P riding services. Since users have the freedom to choose, such allocations are not provided in a forced manner, but usually in forms of personalized recommendation or search. This project aims at developing machine learning methods for economic analysis of the Web economy. In particular, we develop machine learning algorithms over large-scale data to divise recommendation and resource allocation methods for the Web economy. By integrating machine learning with principled economic theories or intuitions, we can achieve targeted goals in online resource allocation, such as intelligent marketing, welfare distribution between consumers and producers, improving economic efficiency, and cost reduction in terms of price, time, location, etc.
Related Publications:Conversational AI
Among the many techniques that compose an intelligent Web, a Conversational System (such as Google Now, Apple Siri, and Microsoft Cortana) is one that serves as the direct interactive portal for end-users, which is expected to revolutionize human-computer interaction in the coming years. With recent progress on IR, NLP and IoT, such systems have also been deployed as physical devices such as Google Home, Amazon Echo, and Apple HomePod, opening up more opportunities for applications in a smart home. Due to users' constant need to look for information to support both work and daily life, a Conversational Search or Recommendation system will be one of the key techniques. Conversational search and recommendation aim at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations.
Related Publications:Product Search
Product search in e-commerce exhibits unique challenges compared with general web search. Because user preferences may be different even on the same type of products, the product search task can be highly personalized in nature. Different from document search, products are described by a hybridization of structured and unstructured data, including product description, image, user ratings/reviews, structured knowledge base, etc. Product search is also a relatively more difficult task because we not only care about user clicks but also user purchase behavoirs, and purchase behaviors cost a lot more than simply clicking a result and are thus more sparse. The product search task is also closely related to the system profits in e-commerce by prodiving satisfactory results that can attract user purchase.
Related Publications:Deep Learning for Recommendation and Search
Our research on deep learning for recommendationo mostly focus on representation learnining for recommendation. To do so, we incoporate multimodal and heterogeneous information sources for user personalization and item recommendataion, and the tasks include top-N recommendation, sequential recommendation, multimedia recommendation, etc. Our Joint Representation Learning (JRL) model achieved 2~3 times of improvment against traditional shallow models for top-N recommendation (N=10), in terms of NDCG, Precision, Recall, and Hit-Ratio, on several product domains of a standard Amazon dataset.
Related Publications:Sentiment Analysis and Mental Health
Extracting sentimental elements from textual corpora serves as the basis for a lot of higher-level research tasks such as recommendation, search, document summarization, public opinion analysis, intelligent marketing, etc. By integrating statistical and machine learning approaches, we developed a phrase-level sentiment analysis toolkit that extracts 'feature-opinion-sentiment' triplets to construct a context-sensitive sentiment lexicon from large scale user textual reviews. For example, in the product domain of mobile phone, sampled triplets include 'picture-clear-positive', 'battery life-short-negative', 'quality-high-positive', or 'noise-high-negative'. It further contains a module that is capable of matching the triplets contained in a piece of review, and another module that contructs feature-level profiles for online items according to the user reviews made towards a specific product. For more details about the toolkit and its application, please checkout the "Sentires: Phrase-level Sentiment Analysis toolkit" under the Software of my homepage.
Related Publications: