Improving the quality of Web information retrieval using multi-resolution link analysis
The World Wide Web is the most important information source in modern society. The information within the Web is accessed through Web search engines such as Google and Yahoo. These search engines use a global popularity rank during the computation of their results, based on links from all of the pages on the Web. This global popularity rank used by Google is known as PageRank. Using these global popularity ranks biases the search results towards the globally popular Web pages. Our aim is to investigate the effect of a peer popularity rank where popularity is measured only through sites of a similar nature. We perform this task by observing the interaction between Web pages at a finer resolution (as shown in Fig. 1). We expect that using the peer popularity rank will allow search engines to locate Web pages that are more specific to the user needs rather than pages of general popularity. This will improve the quality of information that is delivered by Web search engines and provide organisation amongst the billions of existing Web pages.
We can illustrate our argument with this simple example. If we were to ask a group of people where to buy a toaster, they would direct us to one of the big stores like Myer; this is a popular shop but it is not a specialist in any area. On the other hand if we were to ask a group of people who know a lot about toasters, they would direct us to a specialist shop like “Joe’s toaster emporium” where we would get specialist advice as to which toaster to choose. Please note that the Web was searched for a specialist toaster shop, but all that was returned was Amazon.com (the most popular general store on the Web). This result in itself shows that our research is essential.
To date, our research has lead us to analyse the effect of using Symmetric Non-negative Matrix Factorisation (SNMF) to discover multi-resolution Web communities. We have shown that by using this method, we could potentially provide a 50% increase in the precision of the retrieval results over Google’s PageRank. Further research using SNMF must be performed to examine the relationships between the community ranks and the users’ queries.
