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Six Search Technologies You Should Know About

Published: July 6, 2007

Author: David Rodnitzky

The days of the one-size-fits-all search algorithm are over. As impressed as we all were with Google’s PageRank algorithm back in 2001, the results you’d get from that algorithm today would be laughable in comparison to the results we have come to expect from search engines.

Why? Well, in part because SEOs have gamed the original algorithm to death at this point, but mostly because search technology has gotten so much better since then.

Here, then, are six search technologies that will (or are) reshaping the search landscape.

1. Collaborative Filtering. Loyal readers may wonder why I don’t just change the title of this blog to “Collaborative Filtering Thoughts” since I mention this technology about once a post. Collaborative filtering is technology that matches your interests to people similar to you, best expressed in Amazon’s “People who bought this book also bought . . .” Many Web 2.0 applications are based on this principle, such as StumbleUpon, del.icio.us, and Flixster. I’m very bullish on collaborative filtering, simply because I believe that the “wisdom of crowds” can be far more effective than even the best algorithm for many types of searches (product reviews, restaurant recommendations, someday perhaps even dating!).

Examples of Collaborative Filtering: Collarity, Launchcast, Flickster, Amazon.com.

2. Personalization. As the name implies, personalization uses data a search engine has about you to serve more relevant results in the future. For example, if I continually do searches for “lake trout” and “fly fishing”, a personalization engine will likely conclude that my search for “laker” is not for a basketball team, but rather for a fish. Personalization has the potential to be very powerful, but it also comes with a price – privacy concerns. Ultimately, this technology will only work if consumers really trust a search engine to protect and honor their personal data.

Examples of Personalization: Google Personalization.

3. Semantic Search. Semantic search identifies similarities in words and phrases. Thus, if I searched for “telephone”, the search results might show me results that contained sites about “cell phones.” In the paid search world, this might mean that you buy the word “mortgage” on broad match, but end up getting matched with terms like “refinance” and “home equity” because the search engine considers these words to be semantically related to one another.

Examples of Semantic Search: Hakia, Yahoo “also try” results.

4. Clustering. Clustering, or clustered search, tries to categorize words or phrases into a taxonomy (or groups) of related themes. I might type in “auction” and a clustering engine would show me categories like “Online Auctions” and “Fine Art Auctions” and beneath each category I would find sub-categories like “eBay”, “Ubid” and “Sotherby’s.” Clustering is a great way to provide additional navigation options to users who might feel overwhelmed by the raw search results.

Examples of Clustering: Clusty, eBay search results.

5. Local Search. Local search can be described in two ways – either it’s interactive maps like Google Maps, or it’s geotargeting based on the IP address or user registration information. For example, as a registered user of Yahoo, Yahoo knows that I live in the Bay Area (I gave this info to them when I signed up, and I was actually honest). As a result, I get a lot of ads for local events, car dealers, and Bay Area real estate brokers. But even if I didn’t register, Yahoo could still have a good idea of my location by looking at my IP address, or by cookie-ing my searches.

Examples of Local Search: Google Local, Krillion.

6. Human-Edited Search Engines. As funny as it may sound, humans are making a comeback (here’s hoping I don’t see the top of the Statue of Liberty on my next beach walk . . .). As the number of Web sites multiple, it becomes harder and harder for anyone to filter out the all the noise and get to the good stuff. So rather than do the hard work yourself, why not farm out the effort to someone else? That’s the theory behind search newcomers like ChaCha, Mahalo, and really Wikipedia when you think about it.

Examples of Human-Edited Search Engines: ChaCha, Mahalo.

Phew, that’s a lot of different search options. Ultimately, I’m still throwing my hat in the ring with collaborative filtering, but you can bet that each of the technologies above – as well as many that have yet to be developed – will play a role in the future of search!

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