The results of the case studies are a good reassurance that some links should not be under-valued. With some of the examples above in previous quotes, it appears that only relevant links should be contained within relevant content pages. This is not always the case. A site should have a well-structured site map even though the traffic to this page is poor. A site map is a good backup for finding content that cannot be easily found by traversing various links. So basically, have a map located off the main page that will lead to the pages with high levels of importance.
Another interesting topic they focus on is how users react to other information they come across while surfing a site. While navigating a site, they might find some related info that catches their eye and they branch off to check it out. After their curiosity is fulfilled, they resume their journey to seek out the information they originally wanted to find.
Well what this tidbit states is that when a user branches off to some related page, there should be a link on that page that makes it easier for the user to find their interest. This way a user will not have to hit their browser back button or click on a link to return to their previous page. Possibly the best way to do this would be to have highly desired related links on all related pages in order to keep a surfer moving forward on relevant topical information. You do not want your users to move back to an origin unless they desire something on another topic, for which you always provide that link just in case.
Recommended Reading 2
This paper can get pretty technical but it is a good read nonetheless. The main subject that should be digested is the WUFIS algo (Web User Flow by Information Scent.) If Google uses Outride's technology like it is supposed to be used then this could come in handy. Knowing this will be very useful when wanting to boost the usability of a site. The main function is to determine the quality of links and how a user might traverse those links while in search of their wants and needs.
The main details and descriptions of the WUFIS algo are listed on pages 2-5 in the PDF document linked above. Here are some basic translations of the important variables' functions. If you plan on reading the translations below, you should read the pages mentioned or at least have it opened in another window.. If you have no interest in this subject then skip on over it to the next section.
Don't worry, it is a lot simpler than it looks on paper. If you look at the Figure 2 on page 3 you will understand it better. Here is what they do:
1. They extract all the content and links from a web site. Most all search engines do this when they grab your pages.
2.(T) From the links that they extracted from your site, they create the linkage topology. This means that they create a sort of "family tree" design of your link structure. A layout to know what links point to what pages in your site.
3.(W) This is how many times a keyword(s) appears in your page. Think of keyword density here.
4.(TD.IDF) "Term Frequency by Inverse Document Frequency". This is used to determine how many times a keyword occurs throughout a set number of pages.
5.(WTF.IDF) They will look at the density of keywords in PageA and calculate how many times these keywords occur throughout the rest of your site. This would be used to figure out how much weight they have in relation to the information your provide.
6.(i,j) For example, let's say they want figure out how important KeywordA is in relation to PageA. All they have to do is apply it to the formula (WTF.IDF) and bingo, they have it.
7. Q This would be WebSurferA's information need. WebSurferA is looking for information KeywordA.
8. K These would be some sort of hints, either within or surrounding a LinkA ,that would provide a clue into what LinkA would take UserA to if they clicked on it
9.PS With all this information above they are able to determine the user's need within a link. First they figure in UserA's need for KeywordA. Since it is figured that LinkA is the most likely source to have information on Keyword A, they combine the two to form PS.
So to sum this all up first they need to set what UserA's needs are. UserA's needs are KeywordA. It is figured that PageC offers the best amount of information on KeywordA. On PageA they find that LinkA has the highest probability of leading UserA to PageC.
When UserA reaches PageB, the calculation shows that LinkX has the highest probability of leading UserA to PageC.
The site is determined to have a high rate of usability for KeywordA
Now let's say that while running their simulation, they discover that LinkX on PageB does not provide enough clues to UserA that PageC has information on KeywordA. The system knows that LinkX will lead to PageC yet they determine that if UserA is a real person they might not realize it right away. This is because the calculation of K on LinkY and LinkZ shows the same amount of hints that their links will lead to information on KeywordA.
The usability of a site in this scenario is a lot worse than the previous one
There is a flaw to all this and it has to do with image-based links. It is explained in more detail at the end of page 3 and at the beginning of page 4.
Also be sure to read up on the spreading activation algo they describe on page 4 as well. That will come in handy down below when it gets mentioned again.
(edited by: msgraph at 5:25 pm (utc) on Feb. 13, 2002)