Web Recommendations Systems by K. R. Venugopal & K. C. Srikantaiah & Sejal Santosh Nimbhorkar
Author:K. R. Venugopal & K. C. Srikantaiah & Sejal Santosh Nimbhorkar
Language: eng
Format: epub
ISBN: 9789811525131
Publisher: Springer Singapore
5.3 System Architecture
5.3.1 Problem Definition
Given a Web log that contains URLs of the visited Web pages and a set of categories C, our objective is to download each page, compress using hashing, compute similarity using LD and map them onto categories using Levenshtein Similarity Weight(LSW).
Assumptions: In this chapter, we have assumed that the text portion of the Web pages is most significant for mapping and hence only text has been taken into consideration. We have also assumed that Web pages are always present in the categories, i.e. a category must consist of at least one page for mapping and a browsed page must be mapped to at least one category.
The entire functionality is depicted in Fig. 5.1, which consists of the following activities: 1.Interested Web pages being browsed by the user.
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