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5 Early Recommendation Technologies That Could Shake Up Their Niches

Written by Marshall Kirkpatrick / October 16, 2008 10:44 AM / 7 Comments

strandscleanlogo.pngInternational recommendation technology provider Strands has announced the five finalists in the Strands $100K Call for Recommender Start-Ups. From music to video to pharmaceutical drug development recommendations, these plucky startups from all around the world will now present at the Association for Computing Machinery's Recommender Systems 2008 conference in Switzerland and one will be offered a $100k investment from Strands.

In a world more swamped with content options every day, recommendation technology is poised to make a huge difference in our experience online. We identified recommendation tech as one of the 5 most important trends for 2008 but we may have jumped the gun just a little bit. Below is a quick profile of each of the five Strands finalists working to bring more of this paradigm into the present market, followed by our thoughts on which one we're most interested in.

gravitylogo.pngGravity R&D is a four person team from Budapest University of Technology in Hungary. Strands says the team has built a "magic button" that "provides TV viewers instant personalized entertainment at any given time with relevant program tips instantaneously on customer demand. It automatically schedules recordings with the highest probability on user's interest." The Gravity team has participated extensively in the NetflixPrize, a contest in which thousands of teams have aimed to improve the Netflix recommendation algorithm by 10% accuracy. That contest has a $1 million prize and Gravity is currently in 5th place on the leaderboard there.

sentimetrixlogo.pngSentimetrix is another four person team, this one from the University of Maryland Institute for Advanced Computer Sciences. This startup analyzes text content around the web and "has automated sentiment extraction/analysis/scoring, the ability to find and quantify opinions in text." If this kind of technology is of interest to you, see also our review yesterday of the new BooRah API.

iletken is a mysterious project built by four Turkish college students at Koc University. It balances personal and social behavior to recommend advertisements "based on relevance."

recoon.pngReccoon is a stealth project built by Peter Tegelaar and Dominiek ter Heide in the Netherlands. Ter Heide also worked on the Japanese social learning platform iKnow, which launched an API yesterday. Recoon appears to use the iPhone's GPS, user attention data like Last.fm listening history and the GeoNames reverse lookup API to notify you when you're near the location of an event you might like to participate in.

commendologo.pngCommendo is a four person team from two universities in Austria. Team Commendo is both the Grand Prize winner and in first place for the Progress Prize in the Netflix Prize leaderboard.

Strands describes Commendo like this: "Commendo uses recommendation technologies to optimize the drug design process in the pharmaceutical industry, including speeding up drug development and the minimization of adverse drug reactions."

Our Take

All of these sound interesting but the one we're most excited to learn more about is Commendo, the pharmaceutical drug development recommendation engine. We're dubious about the political and economic world of big pharma, but we love innovation and that's a field where there's enough money and science on the line that there's a premium put on magic. Strands has products for all kinds of industries (we think their banking service is the coolest) but we'd love to see what Strands plus the Netflix Prize champs Commendo can do in pharma research.

Will Any of These Make a Difference?

Could these startups change the world? With a little bit of funding and possible acquisition by Strands, they could. Strands has customers around the world in everything from music to banking and mobile. They have a lifestreaming service, ala FriendFeed, that doesn't seem to be going anywhere yet, but getting backing from Strands is a great step for any little recommendation startup.

Bring on a smart future augmented by powerful recommendation technology!

Disclosure: Strands is an RWW sponsor. We'd have written about recommendation startups anyway, though, because we think they're really cool.


Comments

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  1. Hi guys,

    We are team iletken, the 'mysterious' group :)

    Thanks for bringing this news up. We are looking forward to meet all the finalists at Switzerland. ACM RecSys08 conference is an incredible source for researchers at this topic.

    Posted by: M.Deniz OKTAR | October 16, 2008 3:29 PM



  2. We're really excited to bring some companies to daylight that might have been overlooked otherwise. Recommendation is definitely needed in every vertical to keep good content bubbled up.

    As far as Strands.com, the "yet" is the key and we're building an awesome community that's giving us the feedback that's making our vision for discovery even stronger.

    Posted by: drew olanoff | October 16, 2008 3:38 PM



  3. You guys should check out directededge, they've been doing some cool stuff with the wikipedia data.

    http://pedia.directededge.com/article/Web_2.0

    Posted by: Mario Romero | October 16, 2008 10:19 PM



  4. like the sound of reccoon. finally someone is taking advantage of location based technology

    Posted by: ilanasense | October 19, 2008 1:57 AM



  5. wow, I believe Commendo would be most exciting

    Posted by: Social Media Marketing Blog | October 19, 2008 12:16 PM



  6. Well we're not in the Strands list but our IP has been award winning and cited in every major white paper on recommendation engines and personalization. We're now applying to lifestreaming. http://perssonas.com

    Posted by: Jerry Schuman | October 19, 2008 1:54 PM



  7. Very interesting. Combination of semantic web, LBS and recommender system for locations and events: http://www.astina.ch/astina/branch-1/services-products/recommendation-systems-spidering/

    Posted by: Marc | October 24, 2008 8:04 AM



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