Web 2.0 Tagging and Blogs

Do you use Web 2.0 tagging websites? Well, I do. They are so much handier than the traditional browser bookmarks! I can easily access my favorites’ list at home, at the university campus, at cyber cafes, at airports or train stations. I can always find something interesting by looking at what other people tag. By now millions of Internet users have discovered the advantages of the social web sites like Digg, Fark, Del.icio.us and others. These websites bring huge benefits to webmasters as well. When your visitors tag your content they affirm that it is actually interesting and useful. And since most of the social websites allow favorites lists sharing, or use tags as a scoring parameter to rank news entries, thousands of other members can easily discover and visit your site. So social and tagging websites are indeed a great way to promote a site. By adding links to Web 2.0 tagging resources to your posts you remind the visitors to bookmark your content. Besides, including the post url and title as parameters into the link code simplifies the task of tagging.

How can you do it in your Wordpress blog? Quite easily. You don’t need to have PHP or CSS skills; although you have to understand what your Stylesheet and Main Index Template are for. Interested? Read on!

Paid Search Marketing

Many web marketers see paid search marketing as the fastest way to bring traffic to their online shops. At the first glance the pay-per-click scheme looks easy: you bid on a keyword, higher bids get higher positions (Overture), and web users see your listing among the top results for your target keyword. You don’t have to invest into search engine optimization and link building; you pay for your visitors and hope to quickly get targeted traffic. But did you notice that more and more often you have to raise bids above the threshold of profitability in order to put your listings to the top of paid search results? With money you spend for PPC exceeding your revenues is it worth to do online business at all? Well, it is time to seriously consider promoting your website in natural (organic) search results. Interested? Read on!

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Page-Related Factors

Continued from Search Engine Ranking Parameters List

This group includes a large number of parameters, which determine how the page is presented to search engine spiders. Since webmasters have more control over these features than over any other ranking parameter, page-related factors are often used for SEO abuse. This is why search engine algorithms now assign a relatively low weight to them while concentrating more on off-page ranking factors. Nevertheless having a properly optimized page can still provide considerable benefits. The page-related factors are: Interested? Read on!

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There are dozens of factors influencing the position of a page in SERPs. Every SEO beginner knows at least the three most important parameters: link popularity, quality of incoming links and keyword saturation. In addition to them search engines also consider the following aspects when ranking the search results. Interested? Read on!

Related Link: Search engine optimization specialist such as Effect Web Media will help bring qualified traffic to your site.

Authority Threshold Algorithm (AT(k))

The idea behind AT(k) Algorithm is using only k highest authority weights instead of calculating average weight from every authority pointed by a hub. The parameter k is called authority threshold. A variant of an AT algorithm is MAX algorithm, where k=1, i.e. a hub is as good as the best authority it links to.

In general AT(k) algorithm uses the same formula as HITS. The difference is that when calculating the weight of a hub we consider top k authorities only, i.e. Fk(i) is a subset of outgoing links F(i). If the number of outgoing links |F(i)| is less or equal k than the AT(k) algorithm works exactly the same as HITS.

HUBAVG Algortihm

To overcome the shortcoming of the HITS algorithm of a hub getting a high weight when it points to numerous low-quality authorities, the following refinement was suggested. While using the same formula to calculate authority weights, the hub score h is now averaged by a number of outgoing links |F(i)|:

So in order to achieve a high weight a hub should link good authorities. Unfortunately this approach has its own flaw. Consider two hubs pointing to an equal number of equally good authorities. The two hubs are identical until one puts one more link to a low quality authority. The average sum of the authorities it points to sinks, and it gets penalized in weight. This is quite illogical but can be fixed by using so-called Authority Threshold Algorithm.

HITS Algorithm

This algorithm was first described by Jon Kleinberg in his work “Authoritative Sources in a Hyperlinked Environment” (1998). The idea behind the HITS (Hyperlink Induced Topic Distillation) algorithm is that the authorities and hubs mutually reinforce each other. Authority weight of a page is calculated as a sum of hub weights pointing to it, and weight of a hub – as a sum of weights of authorities pointed to by it. In other words a hub is as good as the authorities linked by it, and vice versa. Interested? Read on!

The link structure of the Web is highly sensitive to page topic. Pages tend to contain links pointing to other pages on the same broad topic, e.g. pages on investment banking often link to other business-related resources but rarely to sports portals. While using offline PageRank scores has an advantage of faster processing, it also creates a situation where some highly linked page receive higher ranking on topics for which they have no authority. A query-time adjustment of the scoring function is necessary to refine the search results. Some algorithms like HITS and Hilltop allow such an adjustment. However these algorithms have their own shortcomings that restrict their efficient use by search engines.

HITS algorithm calculates hubs and authorities in query-time but relies on a relatively small subset of the Web – the immediate neighborhood of a page, since otherwise computation time would be unacceptably long. Hilltop algorithm analyses a query and calculates score values by finding pages that seem to be experts in the query-specific topic. This algorithm restricts itself to popular queries, since it can’t produce score values when no experts for an uncommon search term are found.

Topic-Sensitive PageRank extends the original PageRank idea by adding a query-time topic-sensitive adjustment. Interested? Read on!

The PageRank Algorithm

PageRank extends the idea behind the InDegree algorithm by assigning different weights to the links. Links from high quality pages should make a stronger impact on the rank of a page. Therefore it is not only important how many incoming links a page has, but also how important the pointing pages are.

To determine the authority of internet pages PageRank simulates the behavior of a random web-surfer, Interested? Read on!

The InDegree Algorithm

This simplest algorithm uses page popularity as a ranking factor. Page popularity is measured as a number of incoming links – similar to document citation in the academic world. In the early days of the Web this algorithm was widely used by search engines. The InDegree algorithm is not very effective – we would need to consider links not just from any page but from those which are relevant to the query. Otherwise the algorithm can be easily manipulated by obtaining thousands of links from anywhere in the Web, thus artificially inflating the link popularity (link farms). The popularity a_i of a page i is calculated by a simple formula:

a_i = |B(i)|,

where B(i) is a set of pages pointing to page i, and |B(i)| is a number of elements in the set.

Reference

• Borodin, A, Roberts, G.O., Rosenthal, J.S. and Tsaparas, P. ‘Finding authorities and hubs from link structures on the World Wide Web’. In Proceedings of the 10 th International World Wide Web Conference, Hong Kong, May 2001. Available at http://citeseer.ist.psu.edu/borodin01finding.html