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

该研究计划的目标有两个：1)为多代理系统构建一个有效的信任建立模型；2)为个性化的、有说服力的推荐系统开发一个自适应框架。 1)建立信任模型对于多智能体系统至关重要，在多智能体系统中，当环境中可能存在不诚实的智能体时，智能体需要找到可信的合作伙伴进行交易。到目前为止，信任建模的文献主要集中在提出信任评估模型，帮助代理评估其他代理的可信性。然而，对信托建立的方向略有考虑，这使得代理人能够产生他人的信任，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一个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，并相应地调整其操作过程，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一方法。 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(帮助企业建立对客户的信任)，我们预见到我们提出的信任建立模型对行业非常有用，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。人们提出了几种推荐方法，如协同过滤、基于知识等，目的都是为了提高推荐的准确率。然而，最近的文献证明，提供准确的推荐不足以增加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能够说服用户接受所提供的推荐的推荐系统框架。此外，该框架必须适应任何推荐方法，并根据个人用户的具体特征进行个性化。 我们将设计框架的详细体系结构和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针对个人用户选择合适的说服策略。 我们希望有一个具有说服和个性化能力的适应性框架，与任何推荐系统一起工作，以提高其有效性。 总体而言，上述两个研究目标将为信任建模和推荐系统的研究提供理论上的贡献，并为电子商务、移动商务、社交网络等众多应用领域带来实际意义。

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.