10. Theme-based PageRank (continued)

Taher Haveliwala's
Topic-Sensitive PageRank

The approach of Taher Havilewala seems to be more practical for actual employment. Just like Richardson and Domingos, also Havilewala suggests the computation of different PageRanks for different topics. However, the Topic-Sensitive PageRank does not consist of hundreds of thousands of PageRanks

for different terms, but of a few PageRanks for different topics. Topic-Sensitive PageRank is based on the link structure of the whole web, whereby the topic sensitivity implies that there is a different weighting for each topic.

The basic principle of Haveliwala's approach has already been described in our section on the "Yahoo-Bonus", where we have discussed the possibility to assign a particular imporance to certain web pages. In the words of the Random Surfer Model, this is realized by increasing the probability for the Random Surfer jumping to a page after "getting bored". Via links, this manual intervention in the PageRank technique has an influence on the PageRank of each page on the web. More precisely, we have reached taking influence on PageRank by implementing another value E in the PageRank algorithm:

PR(A) = E(A) (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))

Haveliwala's Topic-Sensitive-PageRank goes one step further. Instead of assigning a universally higher value to a website or a web page, Haveliwala differentiates on the basis of different topics. For each of these topics, he identifies other authority pages. On the basis of this evaluation, different PageRanks are calculated - each separately, but for the entire web.

For his experiments on Topic-Sensitive PageRank, Haveliwala has chosen the 16 top-level categories of the Open Directory Project both for the identification of topics and for the intervention in PageRank. More precisely, Haveliwala assigns a higher value E to the pages of those ODP categories for which he calculates PageRank. If, for example, he calculates the PageRank for the topic health, all the ODP pages in the health category receive a relatively higher value E and they pass this value in the form of PageRank on to the pages which are linked from there. Of course, this PageRank is passed on to other pages and, if we assume that health-related websites tend to link more often to other websites within that topic, pages on the topic health generally receive a higher PageRank.

Haveliwala confirms the incompleteness of choosing the Open Directory Project in order to identify topics, which for example results in a high degree of dependence on ODP editors and in a rather rough subdivision into topics. But, as Haveliwala states, his method shows good results and it can surely be improved without big effort.

However, one crucial point in Haveliwala's work on Topic-Sensitive-PageRank is the identification of the user's preferences. Having a Topic-Specific PageRank is useless as long as we do not know in which topics an actual user is interested. In the end, search results must be based on the PageRank that matches the user's preferences best. The Topic-Sensitive PageRank can only be used if these are known.

Indeed, Haveliwala does supply some practicable approaches for the identification of user preferences. He describes, for example, the search in context by highlighting terms on a web page. In this way, the content of that web page could be an indicator for waht the user is looking for. At this point, we want to note the potential of the Google Toolbar. The Toolbar submits data regarding search terms and pages that a user has visited to Google. This data can be used to create user profiles which can then be a basis for the identification of the user's preferences. However, even without using such techniques, it is imaginable that a user simply chooses the topic he is interested in before he does a query.

10. Theme-Based PageRank (continued)

This article reproduced with permission of eFactory.
© 2002 eFactory Internet-Agentur KG Online-Marketing - written by Markus Sobek
PageRank and Google are trademarks of Google Inc., Mountain ViewCA, USA.
PageRank is protected by US Patent 6,285,999.