Cufoglu, Ayse, Lohi, Mahi and Everiss, Colin (2012) Weighted instance based learner (WIBL) for user profiling. In: 10th IEEE Jubilee International Symposium on Applied Machine Intelligence and Informatics, 26th -28th January 2012, Herl’any, Slovakia. IEEE, pp. 201-205. ISBN 9781457701979Full text not available from this repository.
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.
|Item Type:||Book Section|
|Subjects:||University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)|
|Depositing User:||Rachel Wheelhouse|
|Date Deposited:||12 Jun 2012 13:52|
|Last Modified:||14 May 2013 13:52|
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