Facebook is already very practiced at offering recommendations on-site: its News Feed technology pulls the items out of its Live Feed based on who and what you've shown is most important to you among all your friends and their activities. Facebook knows more about you than probably any other consumer service online, probably more even than Google. Recommendation could in fact become bigger than search, and so this feature could become one of Facebook's biggest moves.

recommendations site="abc.com" height="300" width="400" /> should be replaced by an iframe showing recommendations for the abc website (pending checkin on the server side).

Make no mistake: recommendation could be a huge addition to Facebook's arsenal. Recommendation technologies are something we've covered for years here at ReadWriteWeb. We asked a year ago if Facebook was secretly working on a recommendation technology, though the feature we saw then turned out to be something else.

Beyond just being cool for users, recommendation is compelling for site publishers because it's like pre-emptive search. Everyone wants to give their site owners an opportunity to search for the content they want to find, but even better is prompting them with what's effectively personalized search results as soon as they land on a page. Opt-out/opt-in? This essential question of privacy will be put to the test in many ways, as Facebook continues to extend its system of identity across the web.

Facebook knows enough about you, your interests, your friends, their interests, their friends and their interests too that it should be able to nail recommendations fairly well.

"Bee.tv employs a proprietary algorithm that includes contextual and semantic analysis, collaborative filtering, and thematic push to deliver personalized TV, movie and video content recommendations."

You chime in on eight movies from Superbad to Casablanca. I wound up with The Bourne Supremacy (sure), Observe and Report (eh, probably not) and All Through the Night (never heard of it). It didn't blow my mind but it wasn't crazily out of the park either. Presumably, as I use the service, and rate more offerings, the engine will hone in on my weirdness and before you know it, voila! Kentucky Fried Movie and Cities in the Mist.

Recommendations are broken into TV, Web, Mobile and iPad. A recommendation filtering mechanism can monitor your preferences.

YouTube and most online video viewing sites have recommendation algorithms, but Bee.tv is a stand-alone site, with an emphasis on the recommendation process.
The site provides you with a place to purchase each of its recommendations that are for sale, but unlike Hulu, say, it does not seem to be a platform for free programming.

Bee.tv's partners include Apple, YouTube, Tribune Media, Amazon, broadcast and cable networks and various online content creators.

Last April, we held a contest asking readers for their web 3.0 definitions. Our favorite came from Robert O'Brien, who defined Web 3.0 as a "decentralized asynchronous me."

"Web 1.0: Centralized Them. Web 2.0: Distributed Us. Web 3.0: Decentralized Me," he wrote. "[Web 3.0 is] about me when I don't want to participate in the world. It's about me when I want to have more control of my environment particularly who I let in. When my attention is stretched who/what do I pay attention to and who do I let pay attention to me. It is more effective communication for me!"

What O'Brien was getting at is basically what Kiss was getting at: personalization and recommendation. And that's the promise of the Semantic Web. The easiest way to sell the Semantic Web vision to consumers is to talk about how it can make their lives easier. When machines understand things in human terms, and can apply that knowledge to your attention data, we'll have a web that knows what we want and when we want it.

ReadWriteWeb contributor Sramana Mitra put it another way on this blog last February, when she said that web 3.0 will be about adding context to personalization. "Personalization has remained limited to some unsatisfactory efforts by the MyYahoo team, their primary disadvantage being the lack of a starting Context," she wrote. "In Web 3.0, I predict, we are going to start seeing roll-ups. We will see a trunk that emerges from the Context, be it film (Netflix), music (iTunes), cooking / food, working women, single parents, ... and assembles the Web 3.0 formula that addresses the whole set of needs of a consumer in that Context." Or in other words, web 3.0 will be about feeding you the information that you want, when you want it (in the proper context).

Of course, the versioning of the Internet is kind of silly, and probably shouldn't keep going, but it is a fun way to look to the future and predict what we might be coming our way. What do you think of Kiss's idea about web 3.0 being about recommendation and personalization?

Some of the topics we expect to discuss in the show:

• Measuring effectiveness of recommendation systems
• Draw backs / problems (referring to our recent post 5 Problems of Recommender Systems)
• Opportunities our guests see for innovation in the space

We welcome your suggestions for discussion points, either in the comments here or by tuning in LIVE to the show - via Calliflower or Facebook - and participating in the chat room.

UPDATE: the audio is available now.

Download MP3

In a world at risk of information overload, where the line between content producers and consumers is no longer clear and where the pace of everyday life is increasing rapidly - I'd say the recommendation engine business is a very smart one to be in. There is ample precedent and this startup is moving into a relatively established field. Richard MacManus lauded the company's previous multi-million dollar investment and our enthusiasm here for this project continues.

The company announced this morning that it has raised a $24 million B round to take its recommendation system far beyond music. The company says it intends to "lead the social recommendation industry." The round was lead by Spanish bank BBVA and with the participating of existing investors from their June round of $25m. That's an insane amount of money and some people are bewildered why MyStrands has been given it. I'm not one of those people.

The Initial Product and Sales

The company started with an iTunes plug-in that recommends songs similar to what you're listening to and quickly expanded its offerings to include a full multi-media juke-box platform that lets people personalize public playlists with their mobile phones and profiles from home. With offices in Oregon, New York and Barcellona, Spain - this is a web 2.0 company with brisk sales already. The company says it will bank $12 million in 2007. That's no mean feat.

Why This is Very Smart

Music, however, is just the first of many areas of engagement for MyStrands. A huge part of the Amazon.com story is its product recommendation process. Netflix has some of the world's top scientists racing each other to outdo its in-house recommendation engine for a million dollar prize. Last.fm's music recommendation community went to CBS for $280 million. StumbleUpon built web page recommendation into a tasty morsel for eBay to scoop up.

Now MyStrands has a war chest to hire top scientists and bring try and take recommendation to the next level for any type of data. If you can't see the value in that, then you're probably not paying attention.

MyStrands would be a great place to see attention data made real, be it in APML or some other open data standard. This is the kind of company that could create piles of that data or make great use of inbound Attention Data for superior recommendations. MyStrands' recent hire of Scott Kveton, Chair of the OpenID Foundation, to be the company's Director of Open Platforms makes me think that something like that is probably in the works. Kveton says the company is "looking closely at APML, as well as working on some other 'open formats' for describing user taste data. The gist is, the users own this data and we want to give them as much control over it as possible."

When I first saw MyStrands several years ago I thought it looked like a trivial and akward little iTunes plug-in. Like so many startups, though, this company had a much bigger vision all along. Now that vision has $55m in backing, is making big hires and is will record $12m in sales already this year. I'd say this is one company you've got to keep an eye on.

The feature O'Neil wrote about appears to be nothing more than the latest FriendFeed rip-off: truncating repetitive activities. (Ex-Googler Paul Buchheit's FriendFeed is like a Facebook R&D lab without stock options.) Whether Facebook is doing more than that publicly or not, you know they have to be working on recommendation behind closed doors.

O'Neill wondered whether this feature might give the user an option to view other items from the same or other users that Facebook deemed similar to the original post. I say I'm drinking coffee and Facebook shows me a movie, a picture and another message about coffee from one of my friends this morning. That would be pretty awesome for us as users and it would increase ad impressions for Facebook.

More likely is the explanation offered by AllFacebook readers, that this new feature is just a way to scrunch up items that are basically the same so users can't spam their friends' newsfeeds and so that newsfeeds are more pleasing to scan down. In other words, in the image above, the two-headed person on top probably posted about "besplatno-ing" like four times in a row. Facebook decided to show just one of those messages and add a link to view the rest.

That's how FriendFeed does it and it works really well. This seems like a plausible explanation of this screenshot, but it's also a real lost opportunity. Facebook's corny "your friend is a fan of this advertiser's stuff" may be more creepy than compelling - but automated recommendations of all types of items could be great.

We're big fans of recommendation technologies here at ReadWriteWeb, from relatively simple "people who like X also like Y" to more complicated algorithms. The systems are fun to learn about, but the fact of the matter is that recommendation doesn't have to be hard. The hard part is amassing enough data and interested people to be able to make recommendations. Facebook has plenty of data and people, though its labyrinth of privacy restrictions might complicate things a little.

So if this isn't it, and we suspect it is not, we sure do hope that Facebook will soon surface the recommendation technology we assume they are working on behind the scenes.

Yesterday, TechCrunch broke the news that Google had acquired Twitter sentiment analysis and recommendation engine fflick. Today, YouTube posted its blog that it, a subsidiary of Google, had actually done the acquiring.

According to the post, YouTube will be using the "technical talent, design instincts and entrepreneurial spirit of the Fflick team" in its effort to roll out more features "that help you enjoy and discover new videos to watch."

We've always believed that there are great conversations happening all the time off of YouTube.com, and that commentary has the potential to enrich your experience when watching and discovering video on YouTube itself. So today we're excited to announce we've acquired Fflick, a talented team that analyzes social media data to surface great content and the discussions around it.

According to the post, YouTube content is shared around the Internet to the tune of 400 tweets per minute, with "over 150 years worth of YouTube" watched on Facebook every day.

Using a service like fflick, YouTube could harness all of the surrounding commentary and data to better personalize streaming content. Leanback wasn't the only recommendation service launched by YouTube last year. In July, the company also began offering a music recommendation service. Later, in November, it began testing a video recommendation service called "YouTube Topics".

What will we see next, YouTube?

First entry into Extension's Labs is something the company calls The Super Awesome Music Blog Finder Thingy ™. Enter your Last.fm username and it will recommend new music blogs that have posted music from artists you've listened to the most over the last 30 days. It's not great, yet, but it could make a pretty great feature once more fully baked.

Unfortunately, the recommendations include too many low-quality spammy blogs, blogs that link to torrents (a little less easy to listen to) and generally need some refinement.

Extension.fm was founded by Dan Kantor, the creator of AOL-acquired Streampad and the feature in Yahoo's Delicious that renders links to MP3 files playable, and invested in this Spring by Spark Capital, Betaworks, Founder Collective (Caterina Fake, Chris Dixon and others) and Dave Morgan (founder of Tacoda and Real Media).

In other words, chances are good that something interesting is going to happen over there. If that includes recommendations based on data acquired from services all around the web and stored in a central repository, that's cool.

Fans support their favorite artists and bands by purchasing badges. 100% of the money for the badges go to the artists, according to the company's PR representative. As the fan purchases badges and engages in recommendation actions, they earn "rank." That rank gives the fan a chance at "superfan" status and, according to the company, creates opportunities for interactions with the listener's favorite musicians.

The southern California-based company is led by Jason Schultz, of Ambistia business incubation lab.

Fanit launched in preview mode at last month's Coachella music festival in 2010. After this preview, reflecting user input, the interface was updated the game mechanics were refined and it has now relaunched.

Fanit fans can elect to follow other fans and get their list of promoted music and bands.

With its emphasis on recommendation versus in-site play (although there are samples you can listen to) it resembles another site we recently wrote about, Bee.tv. The implied dialogue between musician and fan as well as between fan and fan adds an extra dimension that other music recommendation services, like Pandora and Last.fm, lack.

Whether the implied becomes actual might make the difference between a good idea and a popular service.

Big players like Amazon and Netflix are known for their innovative and effective recommendation engines, the latter of which famously held a contest offering $1 million to anybody who could improve their movie recommendation algorithm.

That's great for companies with sizable budgets and a team of programmers, but how do smaller organizations get into the recommendation game?

Sell Products Through Amazon

Amazon has one of the most impressive and admired recommendation engines, and it has taken 15 years, a massive database of information and plenty of resources to perfect. A small company isn't going to be able to compete with that, but then again, it doesn't have to.

It may seem like a no-brainer, but rather than reinvent the wheel, smaller companies can simply get on board by selling their products via Amazon. By having products for sale on the eCommerce giant's site, companies can ensure their products show up as recommendations for users who view or purchase related products.

Social Media Recommendations

This areas is going to be much harder to influence directly, but by having an active (and preferably well-connected) Facebook or Twitter account, a small business can, in effect, opt into each social network's recommended users feature.

On either site, your ability to show up as a recommended account or page is heavily dependent on who your existing connections are. For example, Facebook tends to recommend Pages to users based on how many of that person's friends like the Page. In time - if this isn't the case already - Facebook could start to utilize other details in its Page recommendations, such as location, type of company and keywords found on the wall or info tab of the Page.

Related Content

While it may not always lead directly to revenue, the content on a company blog can be another area where recommendations can come into play for businesses. Most content management systems utilize some form of related content, typically by using tags or categories. A search for "related posts" on the Wordpress plugins directory, for example, yields 646 results.

While content recommendations are far less technically complex (and lucrative) than product recommendations, they can be an effective way to keep traffic flowing through a site and hopefully find its way to the checkout page.

We encounter recommendation engines online just about every day. What are some more examples of how small or medium-sized businesses can utilize this technology? Leave your thoughts in the comments.

A couple of years ago, Alex Iskold outlined what he saw as the 4 main approaches to recommendations:

• Personalized recommendation - recommend things based on the individual's past behavior
• Social recommendation - recommend things based on the past behavior of similar users
• Item recommendation - recommend things based on the item itself
• A combination of the three approaches above

Amazon: King of Recommendations

In that post, Alex analyzed what Amazon.com - probably the canonical example of recommendations technology on the Web - used to power it's recommendations. Unsurprisingly, he found that Amazon used all 3 approaches (personalized, social and item). Amazon's system is very sophisticated, but at heart all of its recommendations "are based on individual behavior, plus either the item itself or behavior of other people on Amazon." What's more, the aim of it all is to get you to add more things to your shopping cart.

As Xavier identified, other newer Internet companies have tended to focus on specific methods of recommendation. For Pandora, it is a deeper analysis of the item (using its "gene" theory); Strands has taken a boatload of VC money to try and become the number 1 social recommendations provider on the planet; and Aggregate Knowledge is taking more of a behavioral approach.

Google: Focus on Personalized Recommendations

The most successful Internet company of this era has without a doubt been Google. It too has been using recommendation technologies to improve its core search product. There are two ways that Google does this:

1. Google customizes your search results "when possible" based on your location and/or recent search activity;
2. When you're signed in to your Google Account, you "may see even more relevant, useful results based on your web history."

So Google is using both your location and your personal search history to make its search results supposedly stronger. This is very much the 'personalized recommendation' approach - and indeed personalization has been a buzzword for Google in recent years. However, the two other types of recommendation are also present in Google's core search product:

1. Google's search algorithm PageRank is basically dependent on social recommendations - i.e. who links to a webpage;
2. Google also does item recommendations with its "Did you mean" feature.

There are surely other ways recommendations technologies are being deployed in Google search - not to mention the range of other products Google has. Google News, its start page iGoogle, and its ecommerce site Froogle all have recommendation features.

ReadWriteWeb Resources for Recommendation Technologies

Let us know what types of recommendation technologies other companies are using. We also invite you to explore using our custom ReadWriteWeb Resources:

• 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 Recommendation Site Search (Visit and Bookmark)

These nanogenres are generated by Nanocrowd's analysis of thousands of movie reviews and commentary from many different data sources. In fact, the three words in each nanogenre are chosen directly from the review data. We liked how easy it was to get in to this application, and its addictive ability to keep us clicking on movie after movie, creating new lists and discovering new movies. In fact, the last movie site that had us so distracted was the Internet Movie Database.

Of course, we couldn't end this post without mentioning some of Nanocrowd's competitors, applications like Clerk Dogs (Ars Technica review), Jinni (another Ars Technica review) and the big players, Amazon and IMDB (yes, they have a recommendation engine too!)

Each of these has some merits. For example, Clerk Dogs will recommend movies based on your movie suggestion in only one step. And they also have some really cool graphs showing match criteria. Jinni requires you to create a login before you can get started, but then you can search for just about anything, such as a genre, plot, mood, or actor name.

How does Nanocrowd stack up against the other guys?  Time will tell, and the site is still being actively developed (in fact logging in to save movie lists isn't supported quite yet), but so far, we like that we can dive in and have a list to take with us to Blockbuster (they have a recommendation engine too, by the way) in five minutes or less.

Sponsored Lyrics Pages?

Interestingly, Last.fm also announced that it will allow sponsors to advertise on these lyrics pages, including the ability to skin the entire page. We cannot help but wonder if this is a prelude to similar advertising options on other parts of Last.fm's site.

800,000 Lyrics

Even though 800,000 is a large number, this still leaves the majority of Last.fm's catalog without lyrics. The music industry has always been highly protective about lyrics and has been playing a cat-and-mouse game with many of the independent (and often user generated) lyrics sites.

LyricFind, too, started as a rogue lyrics site in 2000, but has licensed content from over 1,700 music publishers since then, including EMI, Sony, and Universal BMG.

Overall, adding lyrics to song pages is a smart move by Last.fm, as it turns Last.fm into even more of a one-stop music experience and also gives the service yet another feature that sets it apart from other music recommendation and discovery sites.

All indications point towards a rise in importance by recommendation engines, so this argument deserves examination. From eBay's acquisition of StumbleUpon to the CBS acquisition of Last.fm to this weekend's MSNBC acquisition of Newsvine - recommendation engines are big money. We've covered quite a few startups in this space and I'm sure it will continue to grow in prominence.

Perhaps more importantly, the "Long Tail" of diverse discovery is an important part of the meritocratic and democratic promise of the new web.

Good recommendation engines are also just plain fun.

After just a little consideration, the Wharton study seems more meaningful as a cautionary tale than as a critique of the inherent nature of recommendation engines. In discussing this with others I've found that most people swing quickly from believing the study is either obviously wrong or obviously correct. It's a more complex question than it might seem.

Recommendation engines should strive to be smarter than simply finding that "there is a high correlation between people who liked X and people who liked Y." I would argue, for example, that recommending other users of a system and highlighting their less popular discoveries could be a good way to solve the problem. Getting it right is probably easier said than done, but it seems there's still plenty of potential for recommendation engines to expand the long tail. The study's arguments are important to consider, though.

What the Study Says

A Wharton summary of the paper excerpts the following to explain the study's conclusion: "Because common recommenders recommend products based on sales and [consumer] ratings, they cannot recommend products with limited historical data, even if they would be rated favorably," the authors write. "This can create rich-get-richer effects for popular products and vice-versa for unpopular ones, which results in less diversity."

There's also some discussion of the Facebook app landscape, arguably an environment where the long tail doesn't hold up. See also this related discussion at TechCrunch.

The authors argue that individual users may consistently be exposed to items that are new to them, but we're all exposed to the same new items - resulting in greater individual diversity but less aggregate diversity.

Counter Arguments

The study includes a counter argument from Greg Linden, who helped develop Amazon's recommendation engine. Linden says "recommendation algorithms easily can be tuned to favor the back catalog -- the long tail -- as Netflix does."

The role played by early adopters, "cool hunters", taste makers and advertisers relative to recommendation engines would also be interesting to look at.

My personal fantasy for recommendation engines is this: I want del.icio.us to look at my bookmarks and recommend not just other URLs I might be interested in, but also other users whose tastes are similar to mine. I'd also like to see which of those recommended users tend to find items of interest earliest, so I can prioritize following them.

Repetition, perhaps another way to describe popularity, will probably always drive consumption - but if I can see all of the things that are discovered by people recommended to me then I can use their less popular picks as guidance.

If other metrics are considered, and surely they are in any sophisticated recommendation engine, then what's called "Attention Data" can help augment recommendations beyond merely what's most popular among people with similar interests. (Need an intro to Attention Data? Here's one that could work for you.)

It would be ill advised to reject recommendation engines as dumb popularity machines based on this study, but it is also important to take its arguments into consideration.

Paul Lamere writes a lot on this topic. He's a former researcher at Sun Labs where he explored ways to organize, search for and discover music. He's the author of Music Machinery, one of the best blogs out there about music and technology. Lamere now works at Echo Nest where he manages the company's developer community. Echo Nest is a music intelligence company founded by Tristan Jehan and Brian Whitman, who met at the MIT Media Lab while pursuing their doctorates in music understanding and synthesis research.

Lamere starts with recommendation services for the Beatles. Most services will recommend John Lennon, Paul McCarthy, The Who, Pink Floyd and other similar artists. True, these are reasonable recommendations but they do not exactly help find new music.

Lamere explains The Beatles are tremendously popular so they tend to get paired with other popular artists. The result: The recommender doesn't tell you anything you don't know. If the service looks for similar sounding music, the results won't help you either. Queen, The Rolling Stones... again, you've heard of these bands.

A different approach calls for a recommendation engine, such as what Echo Nest develops, to call on a variety of sources such as music blogs, Wikipedia entries, tags, review, profile pages, news, audio or video - the list goes on.

What ends up being recommended is a lot different than what you get when just relying on the wisdom of crowds. Echo Net recommended The Beau Brummels, The Dukes of Stratosphear, Flamin' Groovies and Emmit Rhodes.

And so Lamere asks this question: Could Emmit Rhodes be the sixth Beatle? Take a listen.

I'd say that's a pretty decent recommendation.

The wisdom of crowds should not be discounted. It plays a role in how recommendation services work. But it's the practice of stitching together multiple data sources that can find results that give you music you would not ordinarily discover.