III: Small: Towards Explainable Recommendation Systems

推荐系统是我们日常生活中必不可少的组成部分。今天，智能推荐系统被用于许多基于Web的系统中。这些系统提供个性化的信息，以帮助人类做出决定。主要的例子包括日常购物的电子商务推荐，就业市场的就业推荐，以及让人们更好地联系在一起的社交推荐。然而，大多数推荐系统只是向用户推荐。他们很少告诉用户为什么会提供这样的推荐。这主要是因为这些系统背后的封闭算法很难解释。缺乏良好的可解释性会牺牲推荐系统的透明度、有效性、说服力和可信度。这项研究将允许以更可解释的方式提供个性化推荐，从而提高搜索性能和透明度。这项研究将通过研究人员使真实系统中的用户受益吗？与电子商务和社交网络的行业协作。该项目开发的新算法和数据集将补充计算机科学和iSchool计划的课程。作品和演示的展示将有助于吸引更多对计算研究感兴趣的受众。最终，该项目将使人类更容易理解和信任机器决策。该项目将探索一种涉及系统设计人员和最终用户的可解释推荐的新框架。系统设计人员将受益于为模型诊断生成的结构化解释。最终用户将受益于收到各种算法决策的自然语言解释。该项目将解决三个基础研究挑战。首先，它将为可解释的决策创造新的机器学习方法。其次，它将开发新的模型来生成自由文本的自然语言解释。第三，它将确定评估解释质量的关键因素。在这一过程中，该项目还将制定综合的可解释性措施，并发布评估基准，以支持可重复的可解释性建议研究。该项目将向信息检索、数据挖掘、推荐系统和更广泛的人工智能社区传播共享数据和基准。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Recommendation systems are essential components of our daily life. Today, intelligent recommendation systems are used in many Web-based systems. These systems provide personalized information to help human decisions. Leading examples include e-commerce recommendations for everyday shopping, job recommendations for employment markets, and social recommendations to make people better connected. However, most recommendation systems merely suggest recommendations to users. They rarely tell users why such recommendations are provided. This is primarily due to the closed nature algorithms behind the systems that are difficult to explain. The lack of good explainability sacrifices transparency, effectiveness, persuasiveness, and trustworthiness of recommendation systems. This research will allow for personalized recommendations to be provided in more explainable manners, improving search performance and transparency. The research will benefit users in real systems through researchers? industry collaboration with e-commerce and social networks. New algorithms and datasets developed in the project will supplement courses in computer science and iSchool programs. Presentation of the work and demos will help to engage with wider audiences that are interested in computational research. Ultimately, the project will make it easier for humans to understand and trust the machine decisions.This project will explore a new framework for explainable recommendation that involves both system designers and end users. The system designers will benefit from structured explanations that are generated for model diagnostics. The end users will benefit from receiving natural language explanations for various algorithmic decisions. This project will address three fundamental research challenges. First, it will create new machine learning methods for explainable decision making. Second, it will develop new models to generate free-text natural language explanations. Third, it will identify key factors to evaluate the quality of explanations. In the process, the project will also develop aggregated explainability measures and release evaluation benchmarks to support reproducible explainable recommendation research. The project will result in the dissemination of shared data and benchmarks to the Information Retrieval, Data Mining, Recommender System, and broader AI communities.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.