You may want to check out http://www.loomia.com/, which is a turn-key service to provide recommendations to media and retail web sites. (Full disclosure: I'm a founder)

Vistors to e-commerce sites are always open to suggestion/education, hence any up-sell/cross-sell strategies that leverage recommendation should address searchers and browsers, and a good UI should offer both avenues on every web page.

That‚Äôs the trick. We don‚Äôt want to answer too many questions about what we‚Äôre looking for. Half the time we don‚Äôt know what we‚Äôre looking for (‚Äújust browsing‚Ä?) and the other half of the time we are incapable of describing it ‚Äì or maybe that‚Äôs just me. Harvard professor Daniel Gilbert details people‚Äôs inability to understand or describe what makes them happy in his book, ‚ÄúStumbling on Happiness‚Ä?. We think that many of those same mental barriers exist for people shopping or searching online, and we believe a good recommendation platform is essential to overcome them.

Thats why Amazon's People who looked at X bought Y steers them towards a choice of buying.

Alex

Thats because they are off for you :)

What do people think about Pandora's approach? Do you think that pieces of music are highly similar objectively or is it all subjective?

Alex

However, I think you missed where the future lies for recommendation engines and personalization.

Recommendation and personalization technology that works across all three screens (your computer, mobile devices and perhaps most importantly your television) will become increasingly necessary as the long-tail of content expands beyond the web and into your living room.

Today’s consumer has access to a bewildering array of programming options when they turn on their television. With programming available on a seemingly endless amount of channels and on-demand services the need to simplify and streamline this superabundance of choice has never been greater. Without guidance and organization, superabundance is overwhelming and chaotic. Television viewers have always enjoyed this catered experience. Now, they need that special attention to insure that their viewing experience is truly relevant. Cable and satellite providers that are answering their call have realized that by providing personal touch they can retain customer loyalty.

One company leading the charge in this television revolution is Cambridge based ChoiceStream.

ChoiceStream’s premier personalization solution is used by the world’s largest entertainment, e-retail, TV and mobile brands, including AOL, Yahoo!, DirecTV, Overstock.com, Movielink, Akimbo and eMusic.

ChoiceStream’s RealRelevance is the only personalization system of its kind to profile both people and content. ChoiceStream automatically classifies all types of content—music, movies, TV shows, games—based on its various characteristics, otherwise known as attributes. ChoiceStream then learns from these attributes and each consumer’s interaction with the content to better understand the consumer’s unique tastes and preferences. ChoiceStream’s patent-pending personalization, Attributed Bayesian Choice Modeling, enables ChoiceStream to learn about a consumer quickly and accurately, avoiding out-in-left-field results that can immediately undermine consumers’ faith in the recommendations they’re receiving.

This personalization technology is currently in place on the Internet and has been changing the distribution models for today’s largest online content providers and retailers for some time. The technology only now is beginning to be implemented by the leading satellite and cable providers. DirecTV recently implemented an online personalized My TV Planner using ChoiceStream’s technology to recommend programming of interest to its 15.5 million viewers. Although these recommendations are currently only available online, this is likely the first step in the migration to the set-top box. Recommendation engines may very well be the answer to maximizing the television experience; just as search engines have been the answer for the Internet.

Search engines have been the most crucial element of consumer discovery on the Internet, but in this newly interactive television environment they lose some of their effectiveness. Search engine algorithms are based on text, information, and links. This philosophy doesn’t necessarily produce the best results in a television environment. Television viewers don’t want to search for programming in an empty box. They want relevant choices provided without having to search. Therefore the idea of pushing programming at viewers based on their previous choices and individual tastes is being adopted.

The key to pushing this relevant programming is the development of a rich classification system for the programming itself. To do that, cable and satellite leaders are enlisting the help of innovative technology vendors to build characteristic indexes around programming and movies available to users. This index contains more than the kind of information a search engine would utilize. For example, when implemented into an on-demand movie service, the index contains certain attributes of each movie. Is the movie thought provoking? Action packed? Satirical?

A viewer may not truly understand the reasons why they chose ‚ÄúThe Breakup‚Ä? and were subsequently recommended ‚ÄúThank Your for Smoking.‚Ä? The correlation between a romantic comedy and a satire isn‚Äôt necessarily obvious to the naked eye. However, the personalization technology can easily dig deeper into these movies attributes and determine that in fact there is a correlation between the two choices. In this case, both films take objective looks at dramatic conflict and although vastly different both leave viewers examining failed relationships. Some may argue that in this sense cable and satellite providers may know you, or at least your choices, better than yourself.

Personalization also offers the key to building more robust advertising models in a world of content everywhere. The mass adoption time-shifting television viewing, and the proliferation of video and programming available online, has given consumers more control than ever over what they watch, when they watch it and how. It’s safe to say the convergence of the three screens means that consumers will no longer sit passively while advertising is pushed their way. Advertisers are using personalization – preference-driven targeting – to help captivate these unleashed consumers, winning their attention with messages that are directly relevant to their needs and interests. Ensuring a more relevant, more rewarding advertising experience is a first step toward building loyalty between the consumer and advertiser.

(Full disclosure: I work with ChoiceStream)

Good post. There is another type of recommendation that I like and that you didn't mention, at least not explicitly - People Recommendations.

That is "Here is Joe, I like his 'stuff' (bookmarks, tags, photos....) who else is like Joe around here?" Or, more suitably, "Here I am, who else here is like me?"

Del.icio.us doesn't have it, but Simpy has it (and has had it for 1-2 years). Here is an example of people similar to me (look on the right hand side). If you click through some of those people, you will see what we have in common: we're all technical, java seems to be a predominant PL, and we are all interested in search, information retrieval, information gathering, etc.

Another interesting people recommendation at Simpy can be found integrated in bookmark search results. For example, here is a search for adaptiveblue. Note the "Related Users" on the right. You are searching for AdaptiveBlue? Well, then these users might be of interest to you.

Thus, Simpy offers these two recommendations, in addition to those offered by del.icio.us:
Person -> People
Search results -> People

I think the most interesting point is there are *currently* two fundamental activities. Recommendation integration is slowly happening in lots of places and will eventually be integrated into these options or become the 3rd fundamental activity.

Note: working on a podcast recommendation site which may mean I have a biased opinion. http://podcasts.grepr.com

It doesn't really fall into any of the categories described above.

I agree that Amazon knows as much about recommendations as anybody; but the accuracy of recommendations comes from their large dataset, not necessarily from the sophistication of their methods. Note that Amazon does not use a 'genome-oriented' classification to find out why one product is bought alongside another--they could never scale that without an open source effort. They also have not rolled out person-to-person recommendations with any success. For a laugh (and testament to Amazon recommendation accuracy) check out this Onion headline:
http://www.theonion.com/content/node/57311

I think that Netflix is ahead of Blockbuster in many ways: they know that recommendations are the key to unlocking the Long Tail (or, put another way, they are trying to find a way to recommend movies to you that cost less for them to license). But their $1M prize is a very inexpensive way to generate buzz around their recommendation system. I think what they're really measuring is not the improvement of the algorithm, but the level of participation in their current recommendations program. As the number of ratings (and recommendation quality evaluations) goes up, so does the quality of their recommendations. Jump-started by a high-profile contest and a (cheap) million-dollar prize. Good move by Netflix.