Establishing Trust in Multi-agent Systems and Developing an Adaptive Framework for Personalized, Persuasive Recommender Systems

建立多代理系统的信任并为个性化、有说服力的推荐系统开发自适应框架

• 批准号: RGPIN-2020-04036
• 负责人: Tran, Thomas
• 金额: $ 2.11万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752510
• 关键词: Establishing; Trust; Multi; agent; Systems

The goal of this proposed research program is twofold: 1) to construct an effective trust establishment model for multi-agent systems, and 2) to develop an adaptive framework for personalized, persuasive recommender systems. 1) Modeling trust is of vital importance to multi-agent systems where agents need to find trustworthy partners for transactions while dishonest agents may exist in the environment. Until now the literature of trust modeling has mainly focused on proposing trust evaluation models that help an agent evaluate the trustworthiness of other agents. However, slight consideration has been given to the direction of trust establishment, which enables an agent to engender the trust of others to increase its chance to be chosen for transactions. To help fill this gap, the first objective of this proposed research is to construct an effective trust establishment model. We'll employ a machine learning approach that allows an agent to collect information from other agents, learn and predict their behaviors and preferences, and accordingly adjust its course of action to establish trust in those agents. Also, we plan to augment this approach by making use of the social structure of relations among agents. By changing the research direction from trust evaluation (helping customers find trustworthy businesses) to trust establishment (helping businesses build trust in their customers), we foresee that our proposed trust establishment model is very useful for industry and has a large commercial application potential. 2) Recommender systems are software systems that help users find information, products and services. Several recommendation methods, e.g., collaborative filtering, knowledge-based, etc. have been proposed, all with the goal of improving the recommendation accuracy. However, the literature has recently witnessed that providing accurate recommendations is not enough to increase the users' perceived acceptance of the recommendations. Therefore, our second objective is to develop a framework for recommender systems that has the ability of persuading users to accept the recommendations provided. Moreover, the framework must be adaptive to work with any recommendation methods, and personalized to the specific characteristics of individual users. We'll design a detailed architecture of the framework and the algorithms that govern how the framework's components work together to achieve the desired results. We plan to use reinforcement learning to guide the selection of appropriate persuasion strategies for individual users. We expect an adaptable framework with persuasion and personalization capabilities that works with any recommender systems to increase their effectiveness. Overall, our above two research objectives should offer theoretical contributions to the respective areas of trust modeling and recommender systems, and bring practical benefits to many applications domains including e-commerce, m-commerce, social networks, etc.

该研究计划的目标有两个：1）为多代理系统构建有效的信任建立模型，2）为个性化、有说服力的推荐系统开发自适应框架。 1）信任建模对于多智能体系统至关重要，在多智能体系统中，智能体需要找到值得信赖的交易伙伴，而环境中可能存在不诚实的智能体。到目前为止，信任建模文献主要集中在提出信任评估模型来帮助代理评估其他代理的可信度。然而，却忽略了信任建立的方向，即代理人能够赢得他人的信任，从而增加其被选择进行交易的机会。为了帮助填补这一空白，本研究的首要目标是构建有效的信任建立模型。我们将采用一种机器学习方法，允许代理从其他代理收集信息，学习和预测他们的行为和偏好，并相应地调整其行动过程以建立对这些代理的信任。此外，我们计划通过利用代理人之间关系的社会结构来增强这种方法。通过将研究方向从信任评估（帮助客户找到值得信赖的企业）转变为信任建立（帮助企业在客户中建立信任），我们预见我们提出的信任建立模型对工业界非常有用，并且具有很大的商业应用潜力。 2）推荐系统是帮助用户查找信息、产品和服务的软件系统。人们提出了多种推荐方法，例如协同过滤、基于知识等，都是为了提高推荐准确性。然而，最近的文献表明，提供准确的推荐不足以提高用户对推荐的感知接受度。因此，我们的第二个目标是开发一个推荐系统框架，能够说服用户接受所提供的推荐。此外，该框架必须能够适应任何推荐方法，并针对个人用户的具体特征进行个性化。我们将设计框架的详细架构以及控制框架组件如何协同工作以实现预期结果的算法。我们计划使用强化学习来指导个人用户选择适当的说服策略。我们期望有一个具有说服力和个性化功能的适应性框架，可以与任何推荐系统配合使用，以提高其有效性。总的来说，我们的上述两个研究目标应该为信任建模和推荐系统各自的领域提供理论贡献，并为电子商务、移动商务、社交网络等许多应用领域带来实际效益。