Modeling Trust in Open, Dynamic Multi-agent Systems and Developing Framework for Predicting Consumer-generated Reviews' Helpfulness

对开放、动态多代理系统中的信任进行建模并开发用于预测消费者生成评论的有用性的框架

• 批准号: 311810-2013
• 负责人: Tran, Thomas
• 金额: $ 1.46万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=677221
• 关键词: Modeling; Trust; Open; Dynamic; Multi

The goal of this proposed research program is twofold: (a) to construct an effective trust model for open, dynamic multi-agent systems, and (b) to develop a framework for modeling the helpfulness of online reviews. Modeling trust is of vital importance to open multi-agent systems where agents need to find trustworthy partners to help fulfill their tasks while deceptive agents may exist in the environment. We plan to build a trust model that works effectively not only when agents can gather sufficient evidence to evaluate the trustworthiness of others, but also when trust evidence is unavailable (e.g., in highly dynamic systems with new agents continually joining and leaving). Our approach is to compute an agent's trust as an accurate function of direct and indirect evidence, and to make use of incentive mechanisms and machine learning to promote honesty and learn the similarities and dissimilarities between new and existing agents. We expect to achieve a trust model that is valuable to many important application domains such as electronic marketplaces, social networks, vehicular ad-hoc networks, cloud computing environments, and peer-to-peer systems, etc. Online product reviews have become a major source of information to help consumers make good purchase decisions. However, due to the vast number of available reviews and their great difference in the level of helpfulness, consumers really need systems that can discover and recommend the most helpful reviews to them. We would like to develop a rigorous framework for inferring the helpfulness of reviews, which uses a probabilistic approach to formulate helpfulness inference as an optimization problem. We will use our preliminary model for helpfulness prediction as a starting point, but will extend it considerably to obtain a more complex model with significantly increased performance and improved accuracy. This framework is expected to be useful for several applications: Search engines, e-commerce websites, online communities and recommender systems can utilize this framework to offer the most helpful reviews, suitable products, services and/or vendors to their users.

该拟议研究计划的目标有两个：（a）为开放、动态的多代理系统构建有效的信任模型，以及（b）开发一个用于对在线评论的有用性进行建模的框架。 信任建模对于开放式多智能体系统至关重要，在开放式多智能体系统中，智能体需要找到值得信赖的合作伙伴来帮助完成任务，而环境中可能存在欺骗性智​​能体。我们计划建立一个信任模型，该模型不仅在代理可以收集足够的证据来评估他人的可信度时有效，而且在信任证据不可用时也有效（例如，在新代理不断加入和离开的高度动态系统中）。我们的方法是将代理人的信任计算为直接和间接证据的准确函数，并利用激励机制和机器学习来促进诚实并了解新代理人和现有代理人之间的异同。我们期望实现一种对许多重要应用领域有价值的信任模型，例如电子市场、社交网络、车载自组织网络、云计算环境和点对点系统等。在线产品评论已成为主要的应用领域。帮助消费者做出正确购买决定的信息来源。然而，由于可用评论数量巨大且其有用程度差异巨大，消费者确实需要能够发现并向他们推荐最有帮助的评论的系统。我们希望开发一个严格的框架来推断评论的有用性，该框架使用概率方法将有用性推断表述为优化问题。我们将使用初步的有用性预测模型作为起点，但将对其进行大幅扩展以获得更复杂的模型，该模型的性能和准确性显着提高。该框架预计可用于多种应用：搜索引擎、电子商务网站、在线社区和推荐系统可以利用该框架为其用户提供最有帮助的评论、合适的产品、服务和/或供应商。