msgraph - 7:42 pm on Feb 12, 2002 (gmt 0)

Analyzing A User's Search Query

Looking at the explanations above, how do you follow through with a user's search to provide the results they need if in fact they do not find their need in the first set of results?

Well for example, when a user searches for a bit of information, you first give them generalized, yet somewhat specific, listings of results that best match up to the topic. The user selects the topic that most likely fits their needs and then they are provided with a more detailed sub-set.

The generalized topics can include information that might interest the user in a detailed or vague manner. The specifics of the results will depend on the user's selection.

How would they personalize this?

Think of some major search engines out there that give you a subset of results that have either been searched by other users or are determined relevant to the topic.

If the user selects the more detailed topic that relates to their interest, then the results will be narrowed down from there with the addition of the keywords in that topic.

If the user does not select the detailed topics, then the user will be provided results that do not reflect on those details.

To break this down, imagine that you are searching for "vacation packages." You are provided with a list of categories that are popular like Hawaii, Las Vegas, Bahamas, etc. There are other listings that offer vacation packages in general. If you click on a general category, the system would realize that you are not interested in Hawaii, Las Vegas, and Bahamas so they would exclude them from the results.

Another option is to give a user the ability to sort the results based on the various results that are stated above: recency, frequency, popularity, link structure, and context.

This ability to sort results is a popular method used by a variety of sites these days and they appear to be very successful in grabbing the user's interest in specific data. For example, Download.com let's the user sort software packages by popularity, date, title, etc. This is great for users who are looking for popularity of a product or the latest release of an application.

It could also be possible to set up an automatic ranking system without user input in order to learn the user's traits. This would help to identify if the current ranking methods do not interest the user. If the user tends to select results that are further on down the list then perhaps some changes in the ranking systems need to take place. These changes can either be made on a global scale for all users or to just a specific user.

Depending on the resources involved for modifying a specific user's results, this would be a perfect option to provide. For example, let's say a user is given results with a higher popularity first, yet this user tends to click on links that point to fresher pages. The results can be customized so that the user gets a list of fresher yet still popular results.

Spreading activation can also be factored in with the how the results are displayed.

This could be used to learn the terms that a user likes to associate with yet are not always provided in the best pattern.

For example, some users might use the terms "travel bargains" while others use the term "vacation getaways." They are both have similar meanings yet there is the possibility that there are differences.

Scenario 1.

A user might be looking for any kind of vacation package so they search for "vacation getaways." The use of "spreading activation" could list some results that show "travel deals" within the title yet offer vacation getaways on their site.

The user tends to select the "spreading activation" results just as much as their original search terms and bookmarks the pages just the same. The system will notice this and will offer these results just as much in the future.

Scenario 2.

Another user searches for "travel deals" but rarely, if ever, clicks on any of the terms provided by "spreading activation", such as "vacation getaways." This user is interested more in specific travel deals and not vacation getaways so therefore they ignore any other listing that does not contain their search query. The system can identify that the user dislikes the use of "spreading activation" in their results so therefore it is not much of a priority in ranking sites.

This would be a great system to provide for many users in displaying personal relevant results. Imagine if you are going to Las Vegas for a night or two and you are searching for cheap hotel rates. All you want to find are sites offering cheap hotel rates, yet you are given a list of sites that offer various 2-3 day packages with other inclusions. In the future when you are searching for hotel rates, the top sites you view will be for those types of rates and not some vacation package. But, there could be users that tend to look at them as well and their personalized results can be modified to fit their pattern.

An ideal method to finalize how the results are displayed would be to let the users decide what results they want displayed. Do they want to see only the bookmark data, Web-crawled data, or a mixture of the two. If both sets are blended it would also be ideal to have the bookmark data highlighted somehow to let the user aware of that data.

(edited by: msgraph at 8:15 pm (utc) on Feb. 12, 2002)