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In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.

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With an increase in web-based products and services, user profiling has created opportunities for both businesses and other organizations to provide a channel for user awareness as well as to achieve high user satisfaction. Apart from traditional collaborative and content-based methods, a number of classification and clustering algorithms have been used for user profiling. Instance Based Learner (IBL) classifier is a comprehensive form of the Nearest Neighbour (NN) algorithm and it is suitable for user profiling as users with similar profiles are likely to share similar personal interests and preferences. In IBL every attribute has an equal effect on the classification regardless of their relevance.In this paper, we proposed a weighted classification method, namely Weighted Instance Based Learner (WIBL), to build and handle user profiles. With WIBL, we introduce Per Category Feature (PCF) method to IBL in order to distinguish the effect of attributes on classification. PCF is an attribute weighting method and it assigns weights to attributes using conditional probabilities. The direct use of this method with IBL is not possible. Hence, two possible solutions were also proposed to address this problem. This study is aimed to test the performance of WIBL for user profiling.To validate the performance of WIBL, a series of computer simulations were carried out. These simulations were conducted using a large user profile database that includes 5000 training and 1000 test instances (users). Here, each user is represented with three sets of profile information; demographic, interest and preference data. The results illustrate that WIBL with PCF methods performs better than IBL on user profiling by reducing the error up to 28% on the selected dataset.

Abstract-It is estimated that 28% of European Union's population will be aged 65 or older by 2060. Europe is getting older and this has a high impact on the estimated cost to be spent for older people. This is because, compared to the younger generation, older people are more at risk to have/face cognitive impairment, frailty and social exclusion, which could have negative effects on their lives as well as the economy of the European Union. The 'active and independent ageing' concept aims to support older people to live active and independent life in their preferred location and this goal can be fully achieved by understanding the older people (i.e their needs, abilities, preferences, difficulties they are facing during the day). One of the most reliable resources for such information is the Activities of Daily Living (ADL), which gives essential information about people's lives. Understanding this kind of information is an important step towards providing the right support, facilities and care for the older population. In the literature, there is a lack of study that evaluates the performance of Machine Learning algorithms towards understanding the ADL data. This work aims to test and analyze the performance of the well known Machine Learning algorithms with ADL data.