An On-Line Personalized Promotion Decision Support System for Electronic Commerce

With the development of the Internet and Electronic Commerce (EC), enterprises have overcome the space and time barriers and are now capable of serving customers electronically. However, it is a great challenge to attract and retain the customers over Internet. One approach is to provide the responsive personalized service to satisfy the customer demand and promote sales at the first time. Hence, in this paper, we propose a decision support system which develops best promotion products based on combinations of different marketing strategies, pricing strategies, and customer behaviors evaluated in terms of multiple criteria. Data mining techniques are utilized to help the business discover patterns to develop on-line sales promotion products for each customer for enhancing customer satisfaction and loyalty. The proposed system consists of four components: (1) establishing marketing strategies, (2) promotion pattern model, (3) personalized promotion products, and (4) on-line transaction model. A simple example is given to illustrate the implementation and application of proposed decision support system

Agent based mobile negotiation for personalized pricing of last minute theatre tickets

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.This paper proposes an agent based mobile negotiation framework for personalized pricing of last minutes theatre tickets whose values are dependent on the time remaining to the performance and the locations of potential customers. In particular, case based reasoning and fuzzy cognitive map techniques are adopted in the negotiation framework to identify the best initial offer zone and adopt multi criteria decision in the scoring function to evaluate offers. The proposed framework is tested via a computer simulation in which personalized pricing policy shows higher market performance than other policies therefore the validity of the proposed negotiation framework.The Ministry of Education, Science and Technology (Korea

A Reputation and Knowledge Based Trust Service Platform for Trustworthy Social Internet of Things

The Internet of Things has attracted a plenty of research in this decade and imposed fascinating services where large numbers of heterogeneous-features entities socially collaborate together to solve complex scenarios. However, these entities need to trust each other prior to exchanging data or offering services. In this paper, we briefly present our ongoing project called Trust Service Platform, which offers trust assessment of any two entities in the Social Internet of Things to applications and services. We propose a trust model that incorporates both reputation properties as Recommendation and Reputation trust metrics; and knowledge-based property as Knowledge trust metric. For the trust service platform deployment, we propose a reputation system and a functional architecture with Trust Agent, Trust Broker and Trust Analysis and Management modules along with mechanisms and algorithms to deal with the three trust metrics. We also present a utility theory-based mechanism for trust calculation. To clarify our trust service platform, we describe the trust models and mechanisms in accordance with a trust car-sharing service. We believe this study offers the better understanding of the trust as a service in the platform and will impose many trust-related research challenges as the future work

Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft

Personalized ECA Tutoring with Self-Adjusted POMDP Policies and User Clustering

An Embodied Conversational Agent (ECA) is an intelligent agent that enables real-time human/computer interaction in natural language. For its rich style of communication, ECA is particularly popular and useful in applications such as education, e-commerce, healthcare, finance, marketing, and business, where a human-like conversation is more attractive to users than traditional keyboard-based interaction. The interest in using ECA in e-learning has become even stronger since the COVID-19 outbreak, and a preliminary investigation has been started by our research group to extend collaborative learning in a virtual environment with personalized ECA tutoring. This thesis document first highlights the prior work of personalized tutoring with ECA, including wavelet transformation for user clustering and face-to-face interaction for quiz-style e-learning. An enhanced approach is then developed to enable self-adjustment of POMDP policies for dialogue management and to allow a more natural way of question/answer style of personalized tutoring with a generic, flexible tutoring ontology. In addition, the proposed approach uses machine learning techniques to adjust knowledge levels of user clustering and evaluates its effectiveness by conducting experiments with real datasets. This research work is projected to further improve online learning with ECA serving as a personal tutor