Abstract
Implicit web usage data is sparse and noisy and cannot be used for usage clustering unless passed through a sophisticated pre-processing phase. In this paper we propose a systematic way to analyze and preprocess the web usage data so that data clustering can be applied effectively to extract similar groups of user. We split the entire process into analysis, preprocessing and outlier detection and show the effect of each phase on Java Application Programming Interface (API) documentation usage data that is collected from our server logs. We use the extracted clusters for web based recommender systems and present the accuracy of the recommendations.
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Alam, S., Dobbie, G., Riddle, P., Koh, Y.S. (2013). Analysis of Web Usage Data for Clustering Based Recommender System. In: Pérez, J., et al. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent Systems and Computing, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-00563-8_21
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DOI: https://doi.org/10.1007/978-3-319-00563-8_21
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