Abstract
This paper describes new machine learning approaches to predict the correct homepage in response to a user’s homepage finding query. This involves two phases. In the first phase, a decision tree is generated to predict whether a URL is a homepage URL or not. The decision tree then is used to filter out non-homepages from the web pages returned by a standard vector space information retrieval system. In the second phase, a logistic regression analysis is used to combine multiple sources of evidence based on the homepages remaining from the first step to predict which homepage is most relevant to a user’s query. 100 queries are used to train the logistic regression model and another 145 testing queries are used to evaluate the model derived. Our results show that about 84% of the testing queries had the correct homepage returned within the top 10 pages. This shows that our machine learning approaches are effective since without any machine learning approaches, only 59% of the testing queries had their correct answers returned within the top 10 hits.
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Xi, W., Fox, E.A., Tan, R.P., Shu, J. (2002). Machine Learning Approach for Homepage Finding Task. In: Laender, A.H.F., Oliveira, A.L. (eds) String Processing and Information Retrieval. SPIRE 2002. Lecture Notes in Computer Science, vol 2476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45735-6_14
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DOI: https://doi.org/10.1007/3-540-45735-6_14
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