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10 Recommended Recommendation Engines

Alex Iskold just posted Rethinking Recommendation Engines, a product type that we here at ReadWriteWeb have explored a lot over the past year or so. In this follow-up post, we present 10 recommendation engines that we like. And we don’t include the obvious ones, such as Amazon, Netflix, last.fm, Pandora. So it’s not a ‘top 10’, don’t panic. We invite you to add your favorites in the comments.

Recommendation engines were included in our toolkit for 2008: What’s Next on the Web. Marshall Kirkpatrick wrote that “the future is likely to be even more swamped in data, social and content options than the web is today. From Google Reader’s recent incorporation of both feed recommendations and shared items in Reader from your contacts in GMail to the ascendancy of services like Last.fm, Pandora and StumbleUpon – recommendation is beginning to make a big splash already.”

Without further ado, here are our 10 picks, compiled from previous ReadWriteWeb posts:


How do you navigate a nearly infinite world of digital data to find the best content for your tastes and needs? Our collective answer to this question is in its infancy, but Oregon based recommendation service MyStrands has raised a whopping $55 million to build on the existing science of recommendation. Definitely the dark horse of the recommendation engines – one to watch.


MatchMine, a Massachusetts company building a cross-platform media recommendation engine, received a $10 million investment from The Kraft Group. The company released an early product called MyMovieMatch in July. See RIA expert Ryan Stewart’s review of the original product for background from this summer.


Zync, a Massachusetts-based startup, operates a local event recommendation engine based around the city of Boston. The site currently lists 30,000 events across 20,000 venues. And even though it only has 355 users, they have amassed almost 9500 ratings.

According to Zync, their recommendation technology uses patent-pending algorithms to recommend events, activities, and restaurants to users based on the input of other, like-minded people. Theoretically, as with any peer recommendation system, this one would get better and more accurate the more people use it.


The team behind SeeqPod, a music search and recommendation engine, believes strongly in what they call “playable search.” SeeqPod trawls the web, indexing all the music files it finds, and then offers them for playback direct from that location. The company knows that because they are not hosting any music files, but are merely offering links to them, they can neatly sidestep copyright and legal concerns.


Scouta is a web app that provides you with media recommendations, based on preferences and interests you display by your selections within the application. If that sounds complicated, think Pandora, but for all media on the web (including media available outside the US). Or think Last.FM without the fuss about neighbors. To be honest, neither of those comparisons is quite right either. It’s more like YouTube, except all the side column content is actually interesting to you.


Music recommendation and discovery engines are hot stuff but what if you could use some of the same juju to better organize the music you already have in your collection? The newly launched Veenix TuneExplorer for Mac does just that. By looking at qualities the company says include “pitch values, pitch variance, fundamental strengths, and a host of other sonic qualities” – the program acts like Pandora within your music collection.


The Filter, a social music recommendation service backed by rock star Peter Gabriel, has released a new version of their software – featuring an improved user interface, a Facebook app and a partnership with Nokia. The Filter is a “playlist creation suite” for iTunes, iPod, iPhone and Apple TV. It works across Windows and Macintosh and it basically allows you to build playlists from the music stored on your PC, Mac, iPod or Nokia mobile phone.

The Filter’s user base is reported to be growing at 25,000 a month. The engine can identify 5 million songs, 4.5m of which have clips (short samples). The Filter works by using Bayesian mathematics and it was developed by physicist Martin Hopkins.


Born out of a closet dislike for “Shrek 2,” Criticker is a new movie review community and recommendation engine that aims to match users with like-minded individuals who share the same cinematic taste. Once you’ve rated 10 movies at Criticker it begins to form what they call a Taste Compatibility Index (TCI) that matches you up with not only other users, but also professional reviewers who share your taste in movies (though, we found that site really doesn’t start delivering usable results until you’ve rated around 50 flicks).


FeedEachother is an RSS Reader built by a former developer from Yahoo! Answers and another now at craft social network Etsy. The interface will feel very familiar to anyone who uses Facebook or Google Reader. The service does a good job of communicating for novice users while offering a feature set that power users will really like.

FeedEachOther recommends feeds “similar” to the ones you’re subscribed to. Recommendation engines are a key way to leverage the network effect of distributed nodes of knowledge – ala social apps online. Big value there for discovery of high value information sources.


StumbleUpon is a “personalized
content discovery” service, which has grown very popular on the Web. Its main feature is serendipity, finding new webpages by clicking through from other pages ‘stumbled’ by users. The app is now owned by eBay and it’s unknown what they might do with StumbleUpon, but recommending new items to buy might be on the cards.


Now it’s your turn: recommend some more recommendation engines in the comments.

Recommendation Resources:
*The Art, Science and Business of Recommendation Engines
*RWW Recommendation Industry Feed Favorites OPML file (save link)
*RWW Recommendation Industry Feeds – Best of Feed (copy and paste to your reader)
*Click to preview the above feeds before subscribing (pop-up window)

*RWW Open Data Sites Search (Visit and Bookmark)

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