A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes

Abstract

The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, "Relevance" is determined based on a user-centric approach that combines both the reputation of the service, the user's current context, the user's profile, as well as a record of the history of recommendations. Our decision making mechanism is adaptive in the sense that it is able to cope with users' contexts that are changing and drifts in the users' interests, while it simultaneously can track the reputations of services, and suppress repetitive notifications based on the history of the recommendations. The paper also includes some brief but comprehensive results concerning the task of tracking service reputations by analyzing and comprehending Word-of-Mouth communications, as well as by suppressing repetitive notifications. We believe that our architecture presents a significant contribution towards realizing intelligent and personalized service provisioning in pervasive environments.

Analysis of the Application for Random Walk in Computer Science, Physics and Mathematical Equations

“…Until now, random walks is still a popular topic which allows numerous scholars to discover new theorems and models that can even be applied to the most recent technologies. The application of random walk has even extended to machine learning where it can be used as a testing solution for entities such as personnel or components, placing a limit on the number of successive failures allowed [3].…”