III: Small: Towards Explainable Personalization

III：小：迈向可解释的个性化

• 批准号: 2007492
• 负责人: Yangfeng Ji
• 金额: $ 50万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007492&HistoricalAwards=false
• 关键词: III; Small; Towards; Explainable; Personalization

Modern personalized information systems are black boxes to their users: computerized oracles give advice, but cannot be questioned. The lack of explanations for the personalized results has precluded broader adoption of personalization techniques in many important but high-risk domains, e.g., healthcare, education, and finance. This project aims to build a generic computational framework for explainable personalization, such that users will be aware of what information has been collected for customizing the system's output, and system developers can detect what type of personalized results will disclose users' privacy, e.g., unveiling his/her gender, age, or health status. This benefits both systems and users in the development of future information systems.The proposed research focuses on two alternative perspectives, i.e., system-oriented and user-oriented explanation generation, in an adaptive fashion. The learning of personalization will be embedded with respect to the learning of explanation, so as to attain both personalization quality and explanation fidelity. In addition to building prototype systems to conduct user studies for evaluation, this project also takes a unique angle from econometric studies to assess the utility of the explanations via the users' revealed preferences. The value of explanation will be measured by the difference between the utilities of a user's decisions with and without explanations, which in turn enables adaptive evaluation and optimization of both explanation and personalization. The research activities will be incorporated into teaching materials in the area of information retrieval and machine learning. The planed outreach to high school students for education about online privacy would increase their awareness of potential risk of privacy breaches resulted from personalized systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代个性化信息系统对用户来说是一个黑盒子：计算机化的神谕提供建议，但不能被质疑。对个性化结果的解释的缺乏阻碍了个性化技术在许多重要但高风险的领域中的广泛采用，例如，医疗、教育和金融。该项目旨在为可解释的个性化建立一个通用计算框架，这样用户将知道为定制系统的输出而收集了哪些信息，系统开发人员可以检测什么类型的个性化结果会泄露用户的隐私，例如，揭示他/她的性别、年龄或健康状况。这有利于系统和用户在未来的信息系统的发展。拟议的研究集中在两个可供选择的观点，即，面向系统和面向用户的解释生成。个性化学习将嵌入到解释性学习中，以达到个性化质量和解释保真度的双重要求。除了建立原型系统进行用户研究进行评估，该项目还采取了一个独特的角度从计量经济学研究，以评估用户的偏好揭示的解释的效用。解释的价值将通过用户在有解释和没有解释的情况下做出的决定的效用之间的差异来衡量，这反过来又能够对解释和个性化进行自适应评估和优化。研究活动将纳入信息检索和机器学习领域的教材。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Comparative Explanations of Recommendations

• DOI: 10.1145/3485447.3512031
• 发表时间: 2021-11
• 期刊: Proceedings of the ACM Web Conference 2022
• 作者: Aobo Yang;Nan Wang;Renqin Cai;Hongbo Deng;Hongning Wang

Explanation as a Defense of Recommendation

• DOI: 10.1145/3437963.3441726
• 发表时间: 2021-01
• 期刊: Proceedings of the 14th ACM International Conference on Web Search and Data Mining
• 作者: Aobo Yang;Nan Wang;Hongbo Deng;Hongning Wang

Graph-based Extractive Explainer for Recommendations

• DOI: 10.1145/3485447.3512168
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM Web Conference 2022
• 作者: Peifeng Wang;Renqin Cai;Hongning Wang

Spectral Augmentation for Self-Supervised Learning on Graphs

• DOI: 10.48550/arxiv.2210.00643
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Lu Lin;Jinghui Chen;Hongning Wang

How Bad is Top-? Recommendation under Competing Content Creators?

顶级有多糟糕？

• 发表时间: 2023
• 期刊: Proceedings of the 40th International Conference on Machine Learning
• 作者: Yao, Fan;Li, Chuanhao;Nekipelov, Denis;Wang, Hongning;Xu, Haifeng
• 通讯作者: Xu, Haifeng