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.

Personalization is the adaptation of the services to fit the user's interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user's personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.