The most useful is Surf Canyon, which offers a “real-time personalized search” that uses an artificial intelligence formula to adapt to the specific demands of the user. The idea is simple: only a small percentage of responses offered by any search engine correspond to what really interests us. Surf Canyon interprets each click as an indication of what may concern them and then finds similar information in the results offered by Google on the following pages (2, 5, 10, etc.) which are then deployed immediately below the newly activated link. The selection improves with every click, saving the user time.

Gordon had some very flattering things to say. After referring to one of the experiments outlined in our research paper, which was recently published by SIGIR, he replied, “A 40% uptick in user satisfaction by any measure is pretty impressive.” Later, after describing a little about the algorithm behind the re-ranking he responded, “That was impressive. An algorithm that recognizes the subtlety of subcontext has some decent IP cred behind it.” He concluded with:

“… I installed Surf Canyon and was fairly impressed. It was more helpful than I expected (though there may be few more skeptical critics of search user experiences than me)… If you’re not a search marketer [who often needs to see the “vanilla” version of the SERP], I’d definitely recommend taking it for a spin.”

Gordon’s next post in the series will be Part II of the conversation with Mark Cramer in which they will “speculate on where search might go in the future.”

As anyone who’s developed an add-on for Firefox is probably aware, Mozilla’s AMO pages offer a multitude of localization options for your add-on’s webpage: 33 different languages, including English, to be exact. This is an unparalleled opportunity to reach and communicate with users virtually everywhere around the world. However, actually translating your AMO page, quickly and economically, can be challenging. Outside of paying large sums for professional translation services, or hitting up foreign language-speaking friends, we know of two good options.

BabelZilla: www.babelzilla.org

While the UI might be a little obtuse and the process a bit complicated, many add-on developers have told us that they’ve had great success with BabelZilla. If you create an account and upload your add-on, volunteers will translate the embedded text for you. If you would also like to have the text on your AMO page translated, you could drop the strings in .property files. We were never quite able to get it to responses, and the activity seems somewhat low, but it’s free so you might want to give it a try. Apparently there are ways to kindly solicit translators on the forums.

Amazon Mechanical Turk: www.mturk.com

This is not free, but for a very modest amount of money you can get your AMO page quickly translated by native speakers. Create an account and then follow the instructions to upload the text of your AMO page. We used the “Basic Open-ended Question” template. On the Properties page you can specify the location of the worker. For virtually all of our translations we chose people living in the country where the language is spoken. The Reward is up to you, but we paid $5-10 per translation for our AMO page. Publish and then wait for the replies to roll in.

Some translations took a while to be returned, but the majority came in under 1 day, and some came back in hours. The average time a worker spent on a translation was 20-30 minutes. In the end, we were able to get 30 translations in just a few days:

Arabic (ar), Catalan (ca), Czech (cs), Danish (da), German (de), Greek (el), Basque (eu), Spanish (es-ES), Finnish (fi), French (fr), Gaeilge (ga-IE), Hebrew (he), Hungarian (hu), Indonesian (id), Italian (it), Japanese (ja), Korean (ko), Dutch (nl), Polish (pl), Brazilian Portuguese (pt-BR), European Portuguese (pt-PT), Romanian (ro), Russian (ru), Slovakian (sk), Albanian (sq), Swedish (sv-SE), Ukrainian (uk), Vietnamese (vi), Chinese (zh-CN) and Taiwanese (zh-TW). We’re still working on Farsi (fa) and Mongolian (mn).

There are some things to look out for.  When you get the translations you might want to drop them into http://translate.google.com/ which, while far from perfect, can help to make sure they are not way off base. Also, if you get a response from someone that took less than a few minutes, he or she probably just got a machine translation, which is not particularly desirable and naturally you could do yourself. Most people will not do this, but sometimes it might be worth specifying that you want a human translation. (As the Mozilla Devloper Wiki indicates, “You are kindly advised NOT to use any automatic online translation, which can bring unpleasant low-quality output to users.”) Lastly, if there are translations that look suspect, you can even go back into Mechanical Turk and set up another job to have the translations proofread by a different native speaker.

In the end it took a bit of effort, but making your hard development work understandable by so many people around the world makes it worth the while. Feel free to contact us should you have any questions or need any help.

Internet users have come to expect search experiences that deliver immediate and relevant results.  Are you ready to take the concept of personalized search to the next level?

Please join me for a webinar on Tuesday, July 13, 2010, 2 PM – 3PM EDT as comScore teams with Surf Canyon to discuss: The Future of Search: The Emerging Power of Real-time Personalized Search.

Register Now

We will explore how the fracturing of the search market and emerging technologies enable searches that are not only relevant but are dynamic, real-time, engagements.  After reviewing the current search landscape and the limitations of personalization today, our experts will demonstrate critical factors that will drive our future search experiences.

You will learn:

• What personalized search is, who does it now, and where current limitations lie
• Where personalized search is heading and how search engines can adapt
• How personalization will effect marketers and agencies, and how to prepare for it

I hope you’ll join me on July 13!

Cheers,
Eli Goodman
comScore Search Evangelist

[Update 7/28/2010] – comScore has now posted the slides and audio for anyone who missed the webinar and would like to check it out.

“Results offered by search engines mix websites that are truly relevant to the context of the user with websites that have a marginal similarity to the query, or that are totally irrelevant… results offered by search engines, which tend to return many irrelevant websites, require the user to perform a manual filtering of results to obtain those that truly reflect his or her interest. The effort employed in this filtering may be high according to the classification accuracy of the results offered by the search engine (HARDTKE, 2009; BRUSILOVSKY, 2009; [B] MICARELLI, 2007; PANT, 2003).”

“According KHOPKAR (2003), search engines that have features to enable user interaction in order to construct a model of user interests have a higher level of complexity of interaction. This interaction can occur in two ways: implicit feedback, in which user actions are captured in a non-intrusive manner, or explicit feedback, which requires the user’s direct intervention… Software such as Surf Canyon (HARDTKE, 2009) uses both modes of interaction, however, lay users have difficulty interacting with additional features such as icons displayed at the end of each link enabling the approximation of similar websites…”

“Surf Canyon is an interactive IR system that dynamically modifies the query results using Web-based personalization… This software is an extension of the user’s browser and operates on various search engines. The personalization of Web query results offered by this tool comes from the combination of implicit and explicit feedback. The classification is based on a user model that infers the immediate interest of the user. Surf Canyon adds an icon at the end of each result offered by the search engine. When you visit the result or click on this icon, Surf Canyon recommends links similar in context to the one clicked (HARDTKE, 2009).”

“User actions that are considered to identify a positive interest in a website are:

• Length of stay on the document between 2 and 30 minutes;
• Action to Save the document on the computer;
• Action to Add the document in the browser favorites.

These choices are given considering the work of Goecks and Shavlik (1999), LIU (2006), [B] MICARELLI (2007) and MANNING (2008), HARDTKE (2009) and also a survey of users described in Chapter II… HARDTKE (2009) comments in his work that a user click on a hyperlink returned by a query already represents an interest in that website…”

“The development of this work meets the discussions exposed by BRUSILOVSKY (2009), HARDTKE(2009), MANNING (2008), [B] MICARELLI (2007), among others, regarding the need to reduce the effort of locating relevant content on the Web by adapting the results obtained from the search engines according to the interests of user.”

We’re flattered and humbled by the continued recognition of our work.

OAKLAND, CALIF. March 9, 2010 – Surf Canyon, the popular developer of real-time search personalization, was selected by the About Web Search 2010 Reader’s Choice Awards as the “Best Search Engine.” The competition included the major search engines in additional to a number of start-ups.

Since April 2006, Surf Canyon has developed technology to improve the relevancy of organic and sponsored results through real-time re-ranking. By observing user actions as the search is being conducted, Surf Canyon is able to disambiguate intent “on the fly” in order to automatically “dig out” relevant results from as deep as page 100, as well as deliver more highly-targeted advertisements. Static search result pages are now effectively transformed into dynamic conversations.

“The real-time and implicit user actions that Surf Canyon uses to improve the searching and finding experience are cutting edge,” states Jim Jansen, Associate Professor at Penn State University. “It is great for finding those ‘deep’ but relevant results when conducting difficult or ambiguous queries.”

The technology is available with Surf Canyon’s own search engine, launched in July 2009 and available at http://www.SurfCanyon.com. It is additionally offered as a browser add-on for Internet Explorer, Firefox and Chrome and has been downloaded over 1.5 million times since being launched in February 2008. The add-on adds a real-time search personalization “layer” directly on top of Google, Yahoo! and Bing, enabling users to benefit from the technology as they search on the major search engines.

The service is currently handling 1 million queries per day, allowing Surf Canyon to accurately study, using blind control-group tests, the objective enhancements offered by real-time search personalization. As detailed in the research paper entitled “Demonstration of Improved Search Result Relevancy Using Real-Time Implicit Relevance Feedback (http://bit.ly/SurfCanyonResearch),” recently published by SIGIR, Surf Canyon’s technology increases the relevancy of organic search results by up to 40%. Further studies have subsequently been conducted to demonstrate the statistically-significant extent to which advertising revenue is enhanced.

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For further information, contact:
Steven Blinn
BlinnPR
TEL: (212) 675-4777 or (415) 578-3438
steven@blinnpr.com

About Surf Canyon
Surf Canyon develops real-time search personalization software. Its patents-pending technology transforms result pages from lists of static links into dynamic conversations, offering users an automated way to find relevant results buried deep within the search pages.

” I reached out to the CEO, Mark Cramer, who agreed to answer some questions about their search engine… By besting Google and with more than 200 million queries to date, Surf Canyon seems to be catching on.”

Here’s one interesting question and answer:

What was your most unusual job?

After my freshman year at MIT (1987) I got a summer job writing software at the Public Defender’s Office in download Boston. They had a database of case “squibs” that they wanted to be able to access through the input of combinations of keywords and Boolean operators. So I wrote one program to crawl the database, extract the words from the squibs and then assemble them in a reverse index. I then wrote a second program with a UI to accept the keyword and Boolean operator inputs, find matches in the reverse index and then print out lists of matching documents. Turns out, it worked great.