Constantino Martins

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.

Constructivist Approach for an Educational Adaptive Hypermedia Tool

The aim of this paper is to present an Constructivism approach for an educational adaptive hypermedia tool. The learning platform has a constructivist approach, assessing the user knowledge and presenting contents and activities adapted to the characteristics and learning style of the student. The platform allows the students and teachers to autonomously create and consolidate knowledge, with permanent automatic feedback and support, through instructional methodologies and educational activities explored in a constructivist manner.

Adaptive Tourism Modeling and Socialization System

This paper presents an undertaken research work about the development of an Adaptive Tourism Modeling System which attempts to correctly model a tourism web application user profile. This paper will follow the methodology used behind the concept of the application, ‘User Modeling as a process’, which consists of three specific modules in order to achieve a clear user modeling technology. The user representation module devises a collection of knowledge representation formalisms that co-exist in order to constitute the system’s view of the user; the user reasoning module is responsible for the execution of several inference tasks as well as the generation of possibly new and valuable information about the user, using the latter module. The user concept that comes out of these two modules will ultimately be used by various kinds of application-level systems, namely a recommender system, therefore instantiating a project-dependent third module.

PCMAT – Mathematics Collaborative Learning Platform

The aim of this paper is to present an Educational Adaptive Hypermedia Tool, PCMAT, Based on Progressive Assessment and adapted to the student model and learning style. The adaptation of the application is based on progressive selfassessment (exercises, tasks, etc.). The learning platform was already implemented, tested and evaluated in learning processes in Basic Schools. Also, the paper defines and evaluates the characteristic of the User Model to be used in the Student Modeling.

Web Intelligence in Tourism: User Modeling and Recommender System

This paper presents a successful attempt at evolving web intelligence in the tourism scenario, namely throughout two main areas: User Modeling and Recommender Systems. The first subject deals with the correct modeling of tourists’ profiles using a wide variety of techniques, such as stereotypes, keywords and psychological models. These techniques, besides presenting user interests with great coherence and completeness, allow for the reduction of several current problems such as the cold start issue, gray sheep individuals and overspecialization. The recommender system, by making use of all user models’ building blocks, brings an interesting, innovative and hybrid nature to the area, with benefits such as behavioral filtering, multi-technique resourcefulness and on-the-fly suggestions. The architecture was already tested in the scope of a prototype regarding the city of Porto, in Portugal.

Personality traits, Learning Preferences and Emotions

In this paper, we describe how a learning platform that takes into account the students learning preferences, the personality and emotions could prompt better learning results. This model will assess the emotional state of the student in an online learning environment by introducing techniques of Affective Computing that can capture the student emotional state and based on that, adapt the course to the characteristics and needs of the student in order to get an improvement in the learning results. Students, as individuals, differ in their social, intellectual, physical, psychological, emotional, and ethnic characteristics. Also, differ in their learning rates, objectives and motivation turning, their behaviour rather unpredictable. Added to emotion, and in order to obtain an effective model for online learning, students personality and learning style are also considered. The architecture developed was tested by a group of students of higher education in Oporto. The results indicated that the model created used can support and improve the students results, verifying that a negative emotional state could influence the learning process.

PCMAT Metadata Authoring Tool

This paper introduces the PCMAT platform project and, in particular, one of its components, the PCMAT Metadata Authoring Tool. This is an educational web application that allows the project metadata creators to write the metadata associated to each learning object without any concern for the metadata schema semantics. Furthermore it permits the project managers to add or delete elements to the schema, without having to rewrite or compile any code.

Intelligent Hybrid Architecture for Tourism Services

This paper presents an artificial intelligence-based architecture to be used in various tourism services, namely the user modeling and recommender system components. Through the use of several machine learning techniques, such as linear models, neural networks, classification and even text mining, a hybrid and complete approach at understanding tourism application users is attained. To assess the advantages of the model, a specific prototype was created, including a complex user modeling process and an advanced and innovative recommender system. The system was tested in the scope of Oporto city, in Portugal.

PCMAT - Mathematics Collaborative Educational System

PCMAT (Mathematics Collaborative Learning Platform) is a collaborative adaptive learning tool based on progressive assessment for Mathematics in Basic Schools. The learning platform is based on a constructivist approach, assessing the user knowledge and presenting contents and activities adapted to the characteristics and learning style of the student. The intelligent behavior of such platform depends on the existence of a tentative description of the student – the student model (SM). The contents of this model and the student most prominent learning style are used by a domain and interaction model to select the most appropriate response to student actions. The SM is used to select the more appropriate learning object according to the student learning stage. However, this selection demands for the access to metadata describing the contents of the learning object. This need leaded to the application of a standard to describe the learning objects (LO). The authoring of LO corresponds to one major maintenance costs present in these applications. PCMAT is able to generate several instances of the same learning object through the parameterization of some features of the learning object. The platform is also able to process student responses in natural language. This project shows how techniques from Adaptive Hypermedia System (AHS) field can improve e-learning based systems in a basic school environment.

Tourism Recommendation System based in user's profile and functionality levels

This paper describes a proposal to develop a Tourism Recommendation System based in users enhanced profiles (composed by basic user information, relations between user and a set of stereotypes and user functionality levels). The main focus of this work is to evaluate if users physical and psychological functionality levels considered in users profiles creation, will produce significant changes in the recommendation results. This work aims also to contribute with a different way to classify points-of-interest (POI) considering their capacity to receive tourists with certain levels of physical and psychological issues that will be described in this paper sections.