elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
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User Modeling for Personalized Web-Based Systems 241<br />
The IE techniques could be applied to user-supplied documents (such as CV, from which<br />
a system could deduce user’s age, education or previous employments [41] or scientific<br />
paper the user submitted to a particular conference, from which a system could deduce<br />
user’s domain of interests and further focus within the domain) or documents found on the<br />
web, where it can be inspired by classical IE techniques.<br />
User Information Extraction<br />
Web Appearance Disambiguation. Paper [7] proposes the Web appearance disambiguation<br />
methods (Link Structure Model, Agglomerative/Conglomerative Double Clustering and<br />
their combination) and uses a social networks of users as a background knowledge. The<br />
Web appearance disambiguation in general is inferring a model that ultimately provides<br />
a function f answering whether or not a Web page d refers to a particular person h, given<br />
a model M and background knowledge K.<br />
Authors attempt to use as little background knowledge as possible and decided user’s<br />
social network to be such knowledge. Therefore, instead of solving one problem, they solve N<br />
interrelated problems: for each person h i in the group H (a group of people H = {h 1 ,...,h N }<br />
who are related to each other), they find Web pages that refer to h i . The group of people was<br />
defined manually based on e-mail correspondence.<br />
The basic idea of Link Structure Model is that Web pages of a group of acquaintances<br />
are likely to be interconnected, while pages of their namesakes would not. However, the<br />
existence of a direct hyperlink from one relevant page to another may be rare. Two pages can<br />
be considered as linked if both contain a hyperlink to the same page, or both are hyperlinked<br />
from one page, or one page can be reached within three hyperlink hops from the other. Yet<br />
another approach can also be considered, for example, two pages are linked if both mention<br />
the same organization. Authors decided that for their purposes, two Web pages are linked to<br />
each other if their hyperlinks share something in common.<br />
Their set of Web pages D is constructed by providing a search engine with queries<br />
t h1 ,...t HN (where t hi is a name of person h i in user’s h social network) and retrieving top K<br />
hits for each one of the query, so that N × K Web pages are retrieved overall. Every page d is<br />
already associated with a personal name t hi , however, it is yet unknown whether the page d<br />
refers to the actual person h or to his/her namesake (or to neither).<br />
Based on a set D, the model M is constructed. Authors defined a Link Structure Graph<br />
over a set of Web pages D as G LS =(V,E) if nodes of the graph are the Web pages (V ≡ D)<br />
and there exists an edge between any pair of nodes d i and d j iff d i and d j are linked to each<br />
other. Than the the Link Structure Model M LS is defined as a pair (C, δ), where C is the set of<br />
all connected components of the graph G LS (note that C 0 ∈ C, where C 0 is the central cluster,<br />
the largest connected component in G LS that consists of pages retrieved by more than one<br />
query) and δ is a distance threshold.<br />
Finally, the discrimination function is defined:<br />
{ 1 if d ∈ C : ‖ Ci − C<br />
f(d, h|M,K) =<br />
0 ‖ < δ,i =0..M<br />
0 otherwise<br />
(8.3)<br />
The intuition behind this definition is that the pages of the central cluster and of a few clusters<br />
that are close to the central cluster are considered to be relevant, while others are irrelevant.