Wink is a search engine that lets you find the results that others think are most relevant. It combines results from all the most popular tagging sites and then adds Google to fill any gaps. The result? Wink uses its TagRank(tm) algorithms to determine which pages are the most relevant while allowing uses to add their own rating.
We caught-up with Wink founder and CEO, Michael Tanne, and asked him to explain the concept behind Wink and his vision of how â€œtaggingï¿½? will impact search results to come.
[Andy Beal] What are Wink’s goals for 2006?
[Michael Tanne] First, we’re working on getting the word out about Wink – it’s a different kind of search engine and it’s not exactly mainstream yet, but for early adopters and social media enthusiasts it has been delivering good results. Second, we will continue to increase the number of sources that we have to include more social bookmarking sites, and other relevant sources that incorporate tags. We’re also going to be working with other groups to advance some of the standards we’re working on for exchanging tags, such as tagomatic.org and tagcommons.org. Finally, we’re going to continue to work on improving the quality of our search results by improving our algorithms and index.
[AB] At what point will Wink be able to solely use its tag search results?
[MT] Wink doesn’t intend to ever rely on Wink tag results alone. Our defined purpose is to provide users with a search across as much of the user intent on the Web as possible. That means indexing bookmarks and tags, ratings and reviews, and other user-generated content from across a large number of sources. Some amount of user intent will be expressed on Wink itself, because it’s logical to do that in the context of search, but people also do these things at other places where the context is right for that, such as on a shopping site, using a social bookmarks tool, on a social news site, etc. And we think that is just as important and valid as tagging they do at Wink.com.
[AB] What do you foresee as the “tipping point” for Wink?
[MT] As far as a tipping point, that probably happens when there are enough users doing enough tagging around the Web and on Wink, that users who do a search on Wink see a measurably better search result – it’s a critical mass of tags, so to speak. At that point there is more of an incentive to use Wink, and that use leads to more user input, which leads to a better experience, and so on.
[AB] What are your thoughts on the future of vertical search? What role will vertical search engines (like Wink) play in the overall search engine landscape in 2010?
[MT] At this point, and for the next several years, vertical search engines will do better within the context of their particular domain than the large search engines. The large search engines have done a great job in large areas such as local and news, and at including quick answers in context – flight numbers, area codes, etc. But when it comes to searching for travel or jobs or events, those are specialized areas where the user is searching for certain fields (or facets) such as location, company, airline, special meals, window or aisle, degree required, etc. Big search engines have trained people to enter a few words into a search box and get 10 results back. They may introduce vertical search but it will be an area where they don’t innovate as fast as those focused on a terrific experience in that specialty. Perhaps some of those will get acquired. Over time, though, I suspect the major search engines will expand and gradually encompass vertical search.
Wink – and companies like Technorati and Feedster for blog search – aren’t so much vertical search, as they are searching a type of information rather than a particular vertical subject area.
Technorati searches blogs – about any subject. Wink searches tags – about any subject. There is room for these approaches for the foreseeable future.
[AB] What measures does Wink have in place to protect its search results against spam/malicious tagging?
[MT] In the world of anti-spam for email, there’s a company Cloudmark [disclosure: Tanne is an investor] which operates a network in which users block spam when they see it and then models take over from there, blocking the spam for millions of users. There’s great power in having an army of users who know spam when they see it. They can be more effective at blocking spam than algorithms alone, as long as is it very easy for them to block things they consider spam, and a model to apply that blocking across the service.
[Update: Michael expands on Wink's answer to spam in the "Comments" section of this post]
[AB] How does Wink determine relevancy? Can you elaborate on the relationship between the standard search algorithms you are using and your TagRank(tm) algorithms?
[MT] Ahhh, you’re asking for the recipe to the secret sauce? I can just say a little about this. We think that user intent can be inferred from user input such as tagging (but that’s just one kind of input) and the amount a Web page is tagged suggests its relevance. That’s helpful but not enough by itself. You have to take into account who tagged the page and in what context. Also relevance tends to change over time. We have thought through and implemented (and patented) a robust set of algorithms around this, and are tuning and refining every day.
[AB] Which of the big search engines do you see align most with your goals for Wink? Which do you think would find the most value in Wink’s offering?
[MT] So far, Google has been the most aligned and we have a good partnership wherein they provide core Web search for us and we serve ads using AdSense (which of course they share in the revenue). That isn’t to say Yahoo or MSN wouldn’t be good partners but we have found Google to be very helpful so far. We see that Yahoo has taken quite an interest in social bookmarking (by acquiring del.icio.us) and social search (with MyWeb and Yahoo360). It’s evident they get this area and are invested in it, but I don’t know if that means they’d be a better partner or more of a competitor. These things have a way of evolving over time
[AB] Thanks for taking the time to tell us more about Wink!