Enhanced User Interest Level Preprocessing Technique for Efficient Web Page Recommendation
Keywords:
Web logs, Preprocessing, Data Cleaning, User Identification, Session Identification, Web page recommendationAbstract
Web based applications play a major role in people day to day activities. Monitoring the users actions are really an interesting and necessary job of the website forecaster to familiarize about their performance, classify the likeminded users, understand the website visitor’s browsing history, reconstruct the website, web recommendation and web personalization. Web logs are the main source to provide sufficient information about the users and achieve the above requirements. Pattern discovery algorithms are applied to the web logs to extract the desirable information. It is mandatory for website analyst to understand the user behavior and interest for many analytical purposes. Web logs take an important role to know about the user behavior. Several pattern mining techniques were developed to understand the user behavior. But, there are no special preprocessing techniques to identify the user interest level and understand their browsing patterns. A special kind of preprocessing technique is needed to improve the quality and efficiency of the pattern mining algorithms. The proposed preprocessing technique performs the preprocessing activities on web logs and also identifies the similar kind of users. The user similarity helps for efficient web page recommendation technique.
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