In the case of Spotify's new Web radio feature, we're not going to go so far as to say that it's a "Pandora killer," but its inclusion in Spotify's desktop client is going to give the up-and-coming streaming service a tangible advantage over the 11-year-old Web radio service.

The recommendations offered up by Spotify Radio are not quite as good as those on Last.fm or Pandora in many cases, but they're pretty solid and the feature has serious potential. We started stations based on a handful of artists across genres and time periods and found the results to be mostly appropriate without being too broad or overly obvious. We even tried a handful lesser known artists from a few decades ago and Spotify was able to rattle off sonically similar tracks.

The feature definitely has its limitations. For one, that stations based on an individual songs (rather than artists) seem limited. Those channels appear to operate as though you'd selected the artist, not the song. By contrast, when you put a specific track into Pandora, it looks for songs with similar aural qualities regardless of genre, time period or other broad characteristics. It does a pretty effective job of pairing up songs that actually sound similar. And if you don't agree, you can always hit the thumbs down button.

The experience certainly varies depending on what you enter. While many stations returned appropriate-sounding results, a station for the band Nirvana mostly brought up other well-known rock songs from the same era, including a slow, cheesy ballad by Aerosmith.

Spotify hasn't divulged what's fueling their recommendations, but it does feel pretty similar to results from The Echo Nest, which powers a number of music apps, including Clear Channel's Pandora cline, iHeartRadio. UPDATE: It is indeed the Echo Nest that's powering Spotify Radio, both companies have confirmed. The recommendation engine has some growing to do before it's a thoroughly viable alternative to Pandora. Still, the mere addition of such a feature to Spotify will make many users that much less prone to load up Pandora.

Spotify Radio is just the latest way users of the streaming service can discover new music. The company recently unveiled a platform for third party apps, included editorially curated selections from Pitchfork and Rolling Stone, as well as more automated recommendations from Last.fm. The app platform and the new radio feature will both be rolled out shortly to desktop users, but you can download a preview here.

Hunch co-founder and Chief Product Office Caterina Fake told Wired in a recent profile that "the ultimate goal of the company is to map every person on the Internet to every object on the Internet, be that a product, a service, or a person."

Why Hunch Exists

The so-called Web 2.0 era of the Web was based on user-generated content and social networking around that. Services like YouTube, MySpace and Flickr (which was co-founded by Caterina Fake) were the success stories of that era.

But now, in 2010, there is too much user-generated content to manually process. What's more, social networking is practically dominated by one company: Facebook. We no longer rely so much on niche sites like Flickr, YouTube, Netflix, Amazon to connect to other people socially. Another aspect to consider is that there's a lot of new data streaming in from sensors, RFID tags and other Internet-connected objects.

The upshot is that we need web services that can help us process all of this data and connect us to the parts that are personally relevant to us.

Opportunities For Startups

The refreshing thing is that these trends are opening up huge opportunities for startups.

GetGlue is another example of a startup aiming to match social data to objects or media. It knows for example that I recently watched Inception and (mostly) liked it. GetGlue can use that piece of data about me, look at my history of other movie likes, connect that to the movie history and preferences that it knows about other people who liked Inception. Ultimately all of that social and 'like' data can be used by GetGlue to recommend other movies to me that I may like to see.

We're early in this era, but both Hunch and GetGlue are busy building up extensive databases about people and what they like (their "taste" data). Not only that, they're slowly perfecting recommendation engines that process this data - ultimately filtering, structuring and personalizing it.

Let us know what other 'beyond social' startups have caught your eye recently.

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.

"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.

The service now also shows display ads on its website, which, to be honest, don't seem to fit into the general design of the site and look like they were just added for the sake of it.

In the Bloomberg interview, Westergreen also acknowledged that Pandora's struggle with the music industry to negotiate royalty rates could still stop the company from becoming profitable, though Westergreen also said that he is optimistic that these negotiations will come to a positive conclusion for Pandora.

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.

Stumble in an Iframe

The DiggBar, of course, sparked a lot of controversy though StumbleUpon's new toolbar does not include a URL shortener. So, unlike the original DiggBar, there is probably little reason to assume that the new StumbleUpon toolbar will steal too much search engine 'juice,' even though it uses an iframe to show the original page. As users aren't likely to share the long StumbleUpon links or use them to link to a site from their own blog, this shouldn't be too much of an issue. But it should be noted that, as far as we can see, StumbleUpon does not return a canonical link which would tell search engines like Google to ignore the StumbleUpon link and index the original page instead.

We talked to StumbleUpon about this earlier today, and the team there didn't seem too worried about this, but instead emphasized that the bar was easy enough to close. It should be noted, though, that whenever a StumbleUpon user shares a story by email, the recipient will see the toolbar by default.

Until just a few weeks ago, StumbleUpon was part of eBay, but now, StumbleUpon is once again an independent company after its founders and investors agreed to buy the company back. It's good to see that the company continues to roll out new products on its roadmap while it is going through yet another transition, though the company is obviously going see a lot of competition from other content discovery services and social networks in the near future.

Why We Need This

Sites like Yelp, Amazon, the iTunes store, and others have been coming under fire for not having trustworthy reviews. Thanks to anonymous user IDs on some sites, reviewers can be anyone with any agenda. Often they are. On Lunch, however, those drive-by reviews contributed by someone associated with the company or product being reviewed (or with an axe to grind) will not be prominently featured. The reason? Lunch.com's Similarity Network.

The Similarity Network

The Similarity Network is probably the most important feature of this new community - without it, Lunch would just be just another Yelp. After signing up, you kick start the matching engine by playing "ExhilaRATE." Although that name is somewhat unintuitive, clicking the link takes you to a section of the site where you can - guess what? - rate things like movies, books, food, sports, politics, animals...whatever. The experience of rating items here is a lot like that of Amazon's recommendation engine. If you've ever killed a few minutes on Amazon training it to get to know you better, you'll find Lunch.com's engine fairly similar.

The difference is that Lunch.com's engine groups things to rate into categories with titles that sound a lot like Facebook Apps (Top Movies of 2009, What's your Favorite Wine?). The Facebook flavor to these "games" makes sense because in the future, Lunch.com will launch a Facebook connected-experience, perhaps even a standalone app. In the meantime, however, you must go to the site to rate items.

The more you rate on Lunch, the better your matches become. You can see your matches and the percentage of compatibility between you and those like you. There are also tag cloud displays that show what items you both like and which ones you don't.

With Lunch, You Can Rate Anything

If you're still wondering why you would migrate away from more mainstream sites to something like Lunch.com, there's another reason this particular community holds appeal: it allows you to make anything ratable. Again unlike Yelp, ratings don't have to focus on products, services, places, etc. They could also be opinion pieces - like what you thought of Michelle Obama's new outfit for example. That opens the door for a much wider range of recommendations and - since you're matched with those like you - those recommendations will be relevant to your interests.

Lunch.com is in private beta, but you can try it now with the invite code "ReadWriteWeb." To use it, just click the link on the right-hand side of the screen that says "Have an invite code?"

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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.

Loomia is similar to Sphere and another app we reviewed recently, Apture. These services all aim to serve up more clickable content options on media websites - which means more user engagement and time spent on site for publishers.

We spoke to Loomia CEO David Marks and asked him how Loomia compares to Sphere, which at first glance appears to have much in common with Loomia. Marks said that Sphere is trying to do "semantic classification", i.e. analyzing the content of an article and recommending further content based on the findings. However Loomia focuses more on the user and so it does behavioral type recommendations. This can result in a more diverse set of topics, because users typically have a range of content preferences. It depends on the article though, said Marks.

Loomia currently has 2 types of deployment:

• Content (e.g. WSJ)
• Video (e.g. Brightcove)

Marks told ReadWriteWeb that video advertising is currently selling well for big media publishers. Accordingly these publishers typically now want to drive users to their videos - and Loomia has a widget to do that.

Marks told us that a lot of their publishers are "dollar focused" this year, therefore recommendations have become more than just an interesting feature on a website - they can drive more advertising dollars. As an example, Marks told us that a media website's Finance section may sell out with ads, but its Politics section may not (fairly common in big media websites). But the Politics section tends to get bigger page views, so to address the imbalance Loomia's recommendations widgets can drive users from Politics to Finance.

We've been looking at how recommendations are being used in the retail sector a lot, and Loomia is a neat example of how the same technology can have real value for the media segment. Let us know in the comments what other recommendation technologies have caught your eye in publishing.

MyBuys' Technology

The services approach means that MyBuys deploys a variety of algorithms and doesn't favor one approach - unlike, for example, ATG, which uses a method it calls "Statistical Relational Learning" (SRL). This is really the crux of the difference between MyBuys and the other companies we've profiled so far. The likes of richrelevance, ATG and Baynote all have a defining technology (usually patented) which for each is the foundation of its recommendations approach.

MyBuys has no specific algorithmic approach. Rather it appears to license technologies from companies such as Blue Martini, BroadVision, MarketLive and Microsoft. However MyBuys does still have a patent on the technology which brings all these disparate algorithms together - it calls it a "patented portfolio of algorithms".

MyBuys' recommendations are a javascript include for their clients' websites - i.e. the heavy lifting is done on MyBuys' servers. Their clients can see their stats in a MyBuys portal, and also summary stats are emailed to the clients.

Understanding Consumers

MyBuys has a team of people that focuses on site performance for its retail clients. This team - which works across all of MyBuys' client base - focuses on driving performance using a variety of tools and processes. They also do experiments for clients to find out what works best. Rosenblum noted that MyBuys is almost always paid on performance.

On its website, MyBuys says that it "creates deep consumer profiles based on both explicit information we collect from you and from shoppers when they sign up for alerts and implicit information we collect as shoppers interact with your site." Rosenblum claims that MyBuys "understands consumers at a deep level", whereas he said that its competitors don't necessarily do. He told us that the Baynote approach is "strange" because they don't focus on the individual, but rather the 'wisdom of crowds' (which he said is a 'lowest common denominator' approach). Further, Rosenblum claimed that many of MyBuys' competitors don't understand the product catalog, that they suffer from the "cold start" problem - i.e. with a new product there is no place to start, unless you know about consumer retail behavior.

Side note: we're sure that MyBuys' competitors would disagree with some of the above assertions, so we welcome feedback from them in the comments below. One thing we've found in this series is that each company in this space is very willing to talk down their competitors! A sign of a very competitive market.

Examples

On to examples of MyBuys' approach. One is World Market, a retailer of furniture and other goods from around the world. It has a 'May we recommend' section on its homepage, which Rosenblum told us is based on MyBuys' algorithms and what other people have done on the site. After the user hits the homepage, MyBuys tracks that user - they know where they came from, they pay attention to what the user clicks on next, and so on. On product pages, there are a variety of different recommendations on the right of the page under the heading 'More great finds'. The categories under this heading can differ (e.g. for some products there may be no 'featured' recommendation).

Another example is Golf Galaxy, a web retailer of golf gear. This has recommendations such as "Other great ideas" and "People also bought". It also serves up recommendations in the shopping cart: "You may also like".

MyBuys doesn't just do website recommendations, it uses email a lot. If they know the email address of the customer, they will send follow-up emails (e.g. if a user abandons the shopping cart). Rosenblum told us that this works very well, however he assured us that emails are 100% opt-in. He said that for every dollar MyBuys drives through the site, another dollar comes through the email channel.

Conclusion

So how effective are MyBuys' recommendations? According to the company, when recommendations engage consumers (i.e. a user clicks on a recommendation), they're 5 times more likely to convert than when there are no recommendations. Rosenblum told us that its clients see an increase of overall site revenue between 5-20%, which is a similar figure to that which other recommendations vendors have given us. The addition of email usually results in even higher conversions, the company claims.

As to how MyBuys compares to its competitors, as we've noted in previous reviews it's very difficult to make a judgment on that. However we're interested to note that a recommendations vendor can compete well in this market without having its own unique patented algorithm. MyBuys pushes the 'services' approach much more than the other vendors. We're sure that some of MyBuys' claims about the competition would be challenged, nevertheless it appears to be a successful business in the retail recommendations market.

The user interface of the desktop player, which looks a bit like Mozilla's Songbird, is slightly more complex than it really needs to be, but it also gives you access to a set of powerful tools that go beyond the core recommendation service. The desktop player, for example, includes a CD burner and some rudimentary support for managing portable players. Thanks to its integration with AudioID, Mufin can also easily find MP3 id information for tracks in your collection that haven't been tagged with the right information yet.

Sort by Sound

At the center of the Mufin Player, is, of course, Mufin's recommendation engine, which allows you to quickly build playlists based on the music similarities between songs. Once you import a new song (Mufin can import your iTunes library, by the way), Mufin automatically analyzes the musical qualities of that song. Based on this, Mufin will then recommend similar songs from both your own collection, or based on information from Mufin.com, which currently features about 5 million tracks. Sadly, Mufin only allows you to play 30-second clips from songs it recommends from its own site.

The problem for Mufin, of course, is that most users already have a favorite music jukebox. Mufin's recommendation features are extremely interesting, and we recommend that you give it a try, but if you like Mufin's recommendations, then using the iTunes plugin might be more worthwhile for you in the long run.

mufin player from mufin on Vimeo.

There's a certain seductive attraction to the idea that collaborative filtering and artificial intelligence could hand us our heart's desire before our hearts even think of it. Think of the time and effort I could save if I didn't have to make decisions about what to eat, buy, wear, listen to, watch or read. When it comes right down to it, free will is a genuine time suck, and seriously cuts into my blogging schedule.

More Noise to Signal

CleverSet, now known as 'ATG Recommendations', is described on ATG's website as "a predictive recommendations service". It's just one part of a suite of e-commerce "optimization services". The company claims that it is differentiated from other recommendations services by its focus on commerce and the fact that it is a stable, profitable company. In the 2008 year the company did $164M revenue "with profitability". That figure includes many services and licensing revenues, of which product recommedations is just one. ATG was founded in 1991 and it did an IPO in 1999, so it does appear to be more experienced than competitors like richrelevance.

Predictive Recommendations

ATG's core product is an e-commerce suite, which it says is used to power hundreds of online store fronts. Its "e-Commerce Optimization Services", which includes recommendations, can be used on other e-commerce sites as well as those powered by ATG. The company told us that their products aim to increase the following 3 things: conversion rates; order value; and customer attention.

ATG calls its approach to recommendations a "blended approach", which aims to predict what the consumer wants to buy next. The recommendations come from a combination of user, site and product data. Elements include purchase history, the time of day, where the user clicked from, what browser they use, product catalog variables, historical shopping information, click-stream data, and more. Out of all this ATG provides what it calls "predictive recommendations".

How is ATG Different From richrelevance?

ATG's approach sounded very similar to that of richrelevance. So we asked ATG: what's different? Bruce D'Ambrosio, founder of CleverSet and ATG's Chief Scientist, responded that richrelevance is similar, but that it is "mostly a subset" of what ATG Recommendations does. He said that ATG is focused on bringing the merchandiser "into the conversation with the visitor", whereas richrelevance perhaps has less focus on merchandiser and more on the user.

D'Ambrosio further said that ATG models the visitor in both current and longer term sessions. The method it uses is called "Statistical Relational Learning" (SRL), whereby ATG integrates information about the actual user with "similar visitors". It also incorporates relational data about the product and "context of use".

Another key piece of data that ATG focuses on is "engagement", by which it means what fraction of users/buyers interact with recommendations. D'Ambrosio said that they see an enormous amount of this engagement activity before users buy products.

The Netflix Prize

As an interesting aside, we asked D'Ambrosio, as a very learned and experienced engineer in this field, what his thoughts were on the Netflix Prize. We mentioned the Napoleon Dynamite problem, whereby products like that are hard to recommend against.

D'Ambrosio replied that to win the Netflix Prize will require a re-definition of the problem, by "dramatically expanding the scope of the information". He said that most of the interesting information required to do Netflix recommendations well is actually off-site - e.g. product data that's not in the catalog on Netflix. He thinks that to win the Netflix Prize, contestants must gather more information about products like Napoleon Dynamite.

Conclusion: Big Player

As well as finding that it's a crowded market, another thing we've discovered in this series is that comparing recommendation engines directly to one another is nearly impossible. Firstly because each customer has different needs and so unless the customer has tried more than one product, they won't be able to accurately compare them. But secondly, each recommendation engine has a different approach and their algorithms are complicated - and well protected. Who knows what is really under the hood of Baynote, richrelevance or ATG.

However, ATG does have a good track record behind it; and we were particularly impressed by the knowledge of Bruce D'Ambrosio - surely one of the company's best assets in recommendations technologies. So if you're an e-commerce shop looking to implement recommendations, ATG seems like a relatively safe bet.

As we noted in our previous post, our series on recommendation engines has shown that every company in this market - including those which create their own platform, like Amazon and Netflix - have differing approaches and ideas on what makes a good recommendation engine. The key to richrelevance's approach, Selinger told ReadWriteWeb, is that people don't shop the same and so different recommendation types will be used for each shopper. This is markedly different from Baynote's approach, which specifically excludes a user's past shopping behavior and instead focuses on real-time community patterns.

In the worldview of richrelevance, shoppers at Amazon are different to the ones at Sears - one of the companies using richrelevance's technology. Furthermore, a person who has a shopping history at a store is different from someone who is totally new to that site. So, unlike Baynote, richrelevance takes into account a user's purchase history - if known.

If we look at an example from Sears, on this item page the richrelevance recommendations display in two places: on the left there is a 'People Who Viewed x Also Viewed' box, and at the bottom of the page there is a 'Top Sellers' section. Selinger told us that if a Sears user has a long purchase history, then they will see recommendations in Sears based on that. We asked if they need to be logged in to Sears as a registered user, but Selinger told us that it is cookie-based and so doesn't take into account their registered Sears user profile.

richrelevance's Technology: Is it Better Than Baynote's?

We were curious about why richrelevance thinks its approach is superior to those that exclude personal user bahavior, like purchase history (such as Baynote). Selinger told us that richrelevance constantly runs A/B tests, just as Amazon does, in order to find out what the most effective methods of recommendation are for any given customer (e.g. Sears) or individual user. This approach leads to using a mix of 'wisdom of the crowds' and 'personalization'.

The theory is that the consumer will tell you what kind of recommendations they like - e.g. at Sears users may like item-based recommendations in certain products, but personalization in other products. Selinger used the analogy of the 'personal shopper'; richrelevance tries different ways to help users shop, finding the best way by trial and error. There are different types of interaction for each customer, said Selinger.

We asked how this approach works for a brand new customer, because presumably there will be no shopper history to use. Selinger replied that richrelevance "works good straight away, but takes a while to get great recommendations". So they may start out with a new customer using existing data that richrelevance owns (e.g. from a similar vendor), and then gather and test data about the new customer.

As for the technology behind richrelevance, David Selinger has termed it "ensemble learning". In a recent blog post, in response to ReadWriteWeb's Guide to Recommender Systems post, David Selinger wrote that "no 'single algorithmic' approach can hope to keep up with today's ever-changing consumer mindset", so richrelevance doesn't try to force retailers and consumers "into a single bucket". Instead Selinger says that richrelevance has "built a system that adapts to the retailer and to each customer in real-time", which is done via "an adaptive type of artificial intelligence called Bayesian Ensemble Learning."

In a comment on a recent RWW post, Selinger claimed that "algorithms like collaborative filtering are a thing of the past" and that ensemble learning is the next generation beyond that.

Conclusion

Ultimately, only the customers of richrelevance and Baynote know if their recommendations are working. Both companies claim that their technology results in higher sales for their e-commerce customers - richrelevance says it results in a "5%-30% sustained sales lift" for its customers. It's difficult for ReadWriteWeb to corroborate those kinds of figures. What we do know is that Baynote is more focused on community behavior, whereas richrelevance takes both community and personal data into account - including purchasing history, which Baynote excludes.

We get the impression that richrelevance's approach is very broad - perhaps too broad? In an email thread with David Selinger, he told us about some of the different forms of recommendations:

"...there can be basic contextual (you're looking at Adidas, here's more Adidas) or social contextual (you're looking at these Adidas, people who looked at it eventually bought this); you can have basic behavioral (yesterday you looked at Adidas, here's more Adidas), or social behavioral (yesterday you looked at these 10 things, people who looked at those eventually ended up buying one of these 3 things); or basic profile (here's something from your wishlist) to social profile (you seem to like rock music, here's some new rock music)."

That's a lot of data that richrelevance is trying to process, in real time. Let us know in the comments whether you like richrelevance's adaptive approach to recommendations, or whether you think Baynote's more focused approach is better.