Grant: Discovery Grants Program - Individual

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.