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Figure 5.15 Repeat of example Web graph
Notice that because of the concentration of surfers at B and D , these nodes get a higher
PageRank than they did in Example 5.2 . In that example, A was the node of highest
PageRank.
5.3.3
Using Topic-Sensitive PageRank
In order to integrate topic-sensitive PageRank into a search engine, we must:
(1) Decide on the topics for which we shall create specialized PageRank vectors.
(2) Pick a teleport set for each of these topics, and use that set to compute the topic-sens-
itive PageRank vector for that topic.
(3) Find a way of determining the topic or set of topics that are most relevant for a partic-
ular search query.
(4) Use the PageRank vectors for that topic or topics in the ordering of the responses to
the search query.
We have mentioned one way of selecting the topic set: use the top-level topics of the
Open Directory. Other approaches are possible, but there is probably a need for human
classification of at least some pages.
The third step is probably the trickiest, and several methods have been proposed. Some
possibilities:
(a) Allow the user to select a topic from a menu.
(b) Infer the topic(s) by the words that appear in the Web pages recently searched by the
user, or recent queries issued by the user. We need to discuss how one goes from a col-
lection of words to a topic, and we shall do so in Section 5.3.4
(c) Infer the topic(s) by information about the user, e.g., their bookmarks or their stated
interests on Facebook.
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