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Kaufer: We tried different approaches We tried to randomly crawl the Web and we thought, How are we going to randomly select out of the billions of pages So we tried crawling from known travel hubs We d start from the Yahoo Travel directory and see where those sites led us We tried to pick out good, interesting information and automatically categorize it That didn t work so well What we call the signal-to-noise ratio wasn t good enough meaning that, when they got our results back, people wouldn t say, Oh yeah, that s what I was looking for We ended up looking at all of the published sources of information newspapers, magazines and manually went through all the websites from all these places to find the ones that had free access to the back issues of their travel articles.

Variable names like glngNewMatterGroupID are much more descriptive and self-documenting than something like NMGID . Finally, although you may not be able to understand the code, its appearance can give you a clue as to how orderly and logical the mind of the programmer is. Good code:

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Then we hired people to read every single travel article we could find on the Net, and classify that article into our database, and write a one-line summary It s a fairly significant effort, and people that we talked to said, You re nuts You ll never finish But if you actually do the math, you realize that you can work through the backlog (it took us a couple of years, but it was only a couple of years) and then can stay current with what s being published without too much of an effort We take half an hour to read an article, on average, and we ll tag that article as being relevant to everything the article talks about.

If the article is about Maui and things to do in Hawaii and these two resorts, whenever you re searching for Maui or things to do in Hawaii or those two resorts, that article will come up If that article happened to mention, The beaches in Maui are much better than the beaches in Fort Lauderdale, and you were to search on the beaches in Fort Lauderdale, that article is not going to come up, because our search isn t keyword-based It doesn t matter if the article happens to mention something; you only want to read the article if it s actually giving you an opinion on the topic you re researching What we ended up with was a much smaller database as measured by the number of documents that we d indexed, but extremely, extremely relevant.

You go to a page about Maui, and every article on that page really is about Maui, sorted to a pretty good degree based upon which article most people would rather read first Would you rather read an article that has a paragraph about Maui in talking about fun beaches around the world, or an article all about beaches in Maui Probably the latter, so that s why the article is sorted first Your experience on TripAdvisor again, this was initially, when we launched the site was very fulfilling, because the information we found was always spot-on We didn t always have something, but what we had was always a match Jumping forward in time as the site grew, all of a sudden now those hundreds of thousands of articles are dwarfed by the user reviews that our visitors have generated.

It s fresher information and tends to be more detailed To many people, it s more reliable There s a whole other theoretical discussion of, Would you rather read a review about a hotel by Aunt Mary you ve never heard of from Bloomingdale, Indiana, or from Frommer s, the trusted guidebook brand And the follow-up.

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question is, Would you rather read 20 reviews from people you don t know, or 1 review from Frommer s Near as I can tell, most people, when given the choice of only one piece of information, will take the Frommer s even though they might be suspicious it s a little old or a little vague. But when you have 10 or 20 reviews, and you have a half a dozen written in the past couple of weeks, you know you re getting an unvarnished and up-to-date version of what you re looking for. And colorful.

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