Oriana Licchelli

Machine learning approaches for inducing student models

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

A RDF-based framework for user profile creation and management

The semantic evolution of the Web has an heavy impact on traditional systems, as the ability to use a formal interoperable language simplifies information exchange between different systems. In order to foster information exchange and to easily connect new functionalities to semantic knowledge bases, in order to be able to use and reuse the valuable knowledge embedded in the existing systems, we designed a plugin-based framework, and used it to connect together different tools and systems developed in the LACAM laboratory. Our pilot project includes user profiling abilities coming from two components, namely Profile Extractor (PE) and Item Recommender (ITR), and storage capabilities implemented by a repository tool called RDFCore.

Learning Usage Patterns for Personalized Information Access in e-Commerce

The World Wide Web is a vast repository of information, much of which is valuable but very often hidden to the user. Currently, Web personalization is the most promising approach to remedy this problem, and Web usage mining, is considered a crucial component of any effective Web personalization system. Web usage mining techniques such as clustering and association rules, which rely on offline pattern discovery from user transactions, can be used to improve searching in the Web. We present the Profile Extractor, a personalization component based on machine learning techniques, which allows for the discovery of preferences and interests of users that have access to a Web site. More specifically, we present the module that exploits unsupervised learning techniques for the creation of communities of users and usage patterns applied to customers of an online bookshop. To support our work, we have performed several experiments and discussed the results.

A methodological approach for designing and evaluating intelligent applications for digital collections

The rapid evolution of Internet services has led to a constantly increasing number of Web sites and to an increase in the available information. The main challenge is to support Web users in order to facilitate navigation through Web sites and to improve searching among the extremely large Web repository, such as digital libraries, online product catalogues, or other generic information sources. The complexity of todays services could be lowered by means of proactive support or advice from the system. The proactiveness could be achieved using dialoguing agents that exploit user profiles to provide personal recommendations. In this paper, we will present a general methodology to cover the entire process of designing advanced solutions for online services. The methodology has been adopted to elicit user requirements for the system developed in the COGITO project, and to evaluate the performance of the final prototype.

Student profiles to improve searching in e-learning systems

European countries have accumulated an enormous quantity of information in Digital Libraries (DLs). Offering seamless universal access to those collections will have a formidable impact on citizens activities. Students could use information in DLs for improving their curricula, but it is difficult to find the exact chunk of material that solves a specific problem. A possible solution is to develop technologies that learn user preferences for customising information search. This paper focuses on a system based on Machine Learning techniques, the Profile Extractor, which automatically builds student models. An experimental session has been performed, evaluating the accuracy of the system.

Employee profiling in the total reward management

The Human Resource departments are now facing a new challenge: how to contribute in the definition of incentive plans and professional development? The participation of the line managers in answering this question is fundamental, since they are those who best know the single individuals; but they do not have the necessary background. In this paper, we present the Team Advisor project, which goal is to enable the line managers to be in charge of their own development plans by providing them with a personalized and contextualized set of information about their teams. Several experiments are reported, together with a discussion of the results.