Application of Fuzzy Logic for User Classification in Personalized Web Search

Classifying web users in a personalised search setup is cumbersome due the very nature of dynamism in user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted within a fuzzy setting. Prior to analysing user behaviour, nature of user interests has to be collected. This work proposes a fuzzy based user classification model to suit a personalised web search environment. The user browsing data is collected using an established customised browser designed to suit personalisation. The data are fuzzified and fuzzy rules are generated by applying decision trees. Using fuzzy rules, the search pages are labelled to aid grouping of user search interests. Evaluation of the proposed approach proves to be better when compared with Bayesian classifier.