Joel Pinho Lucas

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.

Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

Nowadays, there is a constant need for personalization in e-commerce systems. Recommender systems make suggestions and provide information about items available, however, many recommender techniques are still vulnerable to some shortcomings. In this work, we analyze how methods employed in these systems are affected by some typical drawbacks. Hence, we conduct a case study using data gathered from real recommender systems in order to investigate what machine learning methods can alleviate such drawbacks. Due to some especial features inherited by associative classifiers, we give a particular attention to this category of methods to test their capability of dealing with typical drawbacks.

A fuzzy associative classification approach for recommender systems

Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present.

A tool for interactive subgroup discovery using distribution rules

We describe an approach and a tool for the discovery of subgroups within the framework of distribution rule mining. Distribution rules are a kind of association rules particularly suited for the exploratory study of numerical variables of interest. Being an exploratory technique, the result of a distribution mining process is typically a very large number of patterns. Exploring such results is thus a complex task and limits the use of the technique. To overcome this shortcoming we developed a tool, written in Java, which supports subgroup discovery in a post-processing step. The tool engages the analyst in an interactive process of subgroup discovery by means of a graphical interface with well defined statistical grounds, where domain knowledge can be used during the identification of such subgroups amid the population. We show a case study to analyze the results of students in a large scale university admission examination.

UM AGENTE PEDAGÓGICO ANIMADO INTEGRADO A UM AMBIENTE DE ENSINO A DISTÂNCIA

A proposta deste trabalho e desenvolver um personagem pedagogico animado, que e uma excelente alternativa para aumentar a comunicacao e a interatividade em ambientes de ensino, prendendo a atencao e motivando o aluno. Os agentes animados sao capazes de guiar estudantes em acoes. Desta maneira os estudantes podem aprender de um modo mais eficiente. Considerando que eles podem contribuir com melhorias no processo de ensino-aprendizagem, este projeto busca a implementacao dos mesmos no papel de um assistente integrado a um conhecido ambiente: o TelEduc.

Lightweight tourism recommendation

In this paper, we present the tours planning system entitled TOURSPLAN, along with a new lightweight user modelling UM process intended to work as a tourism recommendation system in a commercial environment. The new process tackles issues like cold start, grey sheep and over-specialisation through a rich user model and the application of a gradual forgetting function to the collected user action history. Also, significant performance improvements were achieved regarding the previously proposed UM process.

Proposta de um agente pedagógico animado inserido no ambiente TELEDUC como uma ferramenta tutora

Este trabalho apresenta uma proposta de implementacao de uma ferramenta para ser inserida no ambiente TelEduc, tal ferramenta consiste em um agente pedagogico animado, o qual foi apresentado em trabalho anterior. O objetivo deste trabalho e de melhorar ainda mais a interacao dos alunos com o ambiente, assim como propiciar que estes aproveitem ao maximo os recursos existentes no mesmo. Alem disso, busca-se estimular os alunos a utilizarem o ambiente e que tenham um tempo de permanencia ainda maior no ambiente. A ferramenta proposta podera ser personalizada pelos alunos, de acordo com suas necessidades, e tambem pelos professores, de acordo com as finalidades de cada curso ministrado no ambiente. Desta forma, os alunos poderao aprender de forma ainda mais eficiente.

Mining quantitative class-association rules for software size estimation

Associative models are usually applied in knowledge discovery problems in order to find patterns in large databases containing mainly nominal data. This work is focused on two different aspects, the predictive use of association rules and the management of quantitative attributes. The aim is to induce class association rules that allow predicting software size from attributes obtained in early stages of the project. In this application area, most of the attributes are continuous; therefore, they should be discretized before generating the rules. Discretization is a data mining preprocessing task having a special importance in association rule mining since it has a significant influence on the quality and the predictive precision of the induced rules. In this paper, a multivariate supervised discretization method is proposed, which takes into account the predictive purpose of the association rules.

Fuzzy Data-Mining Hybrid Methods for Recommender Systems

CRM (Customer Relationship Management) is one important area of Business Intelligence (BI) where information is strategically used for maximizing the value of each customer in a company. Recommender systems constitute a suitable context to apply CRM strategies. This kind of systems are becoming indispensable in the e-commerce environment since they represent a way of increasing customer satisfaction and taking positions in the competitive market of the electronic business activities. They are used in many application domains to predict consumer preferences and assist web users in the search of products or services. There are a wide variety of methods for making recommendations; however, in spite of the advances in the methodologies, recommender systems still present some important drawbacks that prevent from satisfying entirely their users. This chapter presents one of the most promising approaches consisting of combining data mining and fuzzy logic.

Applying Recommender Methodologies in Tourism Sector

Nowadays, there is a constant need for personalization in recommender systems. Thus, they try to bring it by making suggestion and providing information about items available. There are numerous options of methods to be employed in recommender systems. However, they still suffer from critical limitations and drawbacks. Therefore, current recommender techniques try to minimize the affects of such drawbacks. In this work we describe two different recommender methodologies proposed. To do so, we implemented such methodologies in a real recommender system for tourism. Afterwards, we analyzed and compared the recommendation given by both methodologies in order to find out if they are effective and able to deal with common drawbacks.