Anisotropic propagation of user interests in ontology-based user models

This work contributes to the development of ontology-based user models, devised as overlays over conceptual hierarchies derived from domain ontologies. We tackle the problem of propagation of user interests in such a conceptual hierarchy. In addition to accounting for the hierarchical structure of the domain and the type and amount of feedback provided by the user, the principal contributions introduced in this work are: (i) horizontal propagation which enables propagation among siblings, in addition to vertical propagation among ancestors and descendants; (ii) anisotropic vertical propagation which permits user interests to be propagated differently upward and downward; (iii) context-dependance which introduces the possibility to propagate differently according to various contexts for specific applications; (iv) support for dynamic ontology maintenance, i.e. preserving the user interest values when adding or removing a node from the conceptual hierarchy. Our approach supports finer recommendation modalities and contributes to the resolution of the cold start problem, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the conceptual hierarchy of the domain. A field evaluation confirmed the effectiveness of our approach w.r.t. the traditional vertical propagation

Inferring semantic relations by user feedback

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion