I’ve read a very good article three days ago called An Early Personalized Recommendation System - Firefly It’s an old article about Firefly … ie. the first recommandation system company. The article is well written and well documented. Slawski the guy behind the blog made me discover (trough the article quoted below) Paul Lamere’s blog (I am nearly confused by the fact that I didn’t find it before !) who just wrote an extraordinary article about Music recommandation plug-in for Itunes called : "The Recommenders Remoras" (just quoted by Greg)
Here is an extract :
One thing that iTunes does not do very well is give new music recommendations. [...] Poor recommendations has driven a whole industry of music recommenders that work with iTunes. [...] These recommenders run as iTunes plugins, sidebars and tag-alongs. When you start iTunes, these programs also start up - they keep tabs on what you are listening to, and use this data to generate recommendations for you. [...] These recommenders are like remoras (aka suckerfish) to the iTunes shark. [...] By attaching itself to a host such as a shark, a remora benefits by using the host as transport and protection and also feeds on materials dropped by the host. The recommender remoras benefit from iTunes reach onto millions of desktops and feed on the opportunities left by iTunes’ poor recommender.
W.Slawski pointed in his post to another article about music recommendation called "Better than we know ourselves" This article decribed and critized the main Music RS (i.e Last.fm / Pandora / Paul Lamere’s project at Sun !!! Incredible Project !!! / as well as MusicIP ….etc).
The article starts with "MUSIC IS TRIBAL IN ITS ESSENCE" but quickly focuse on technology (too bad, despite the fact that it’s a huge point)…. Actually it’s one argument in favour of U.[lik]
that seems to be more and more dominant in my thinking.
"99% of the time, in my experience, the hard part about creativity isn’t coming up with something no one has ever thought of before. The hard part is actually executing the thing you’ve thought of.
The devil doesn’t need an advocate. The brave need supporters, not critics."
I’ve just found a very smart post on recommendation. It’s from read/write… it starts with the Netflix Prize and reminds me of my post called Netflix Prize a new philosophy of innovation (it’s here but in french, Mathieu has also a good post here & in english) and I must say that I fully agree. But the best part is about Recommandation and the difference between search and browse (extract just following).
The analysis is brilliant and takes 3 very very good examples: -Amazon pure collaborative filtering -Pandora pure content based -Delicious pure library (very smart analysis of the nearly reco system from delicious, even if Google similar are another possible way to get the same kind of aswers)
And the author ends with the idea of another approach that mixes the 3 ! Neverthless, no example to illustrate it. So let me offer mine : U.[lik]
U.[lik] is a virtual library of your entertainement because bookmarking matters, and sharing is a huge social leverage
U.[lik] is content based because it enables cool browsing (please read below in red) and content based is perfect for making recommendation in the long Tail (few ratings)
U.[lik] is Collaborative filtering when we have enough data on items and because it’s what matters for a community like people you see movies with or talk about books !
So please Alex Iskold consider taking time reviewing U.[lik] and get over the disruptive presentation. I’ll be available to chat anytime if you need (we are GMT time). PS: take a look at the widget on the left
Browsing and Recommendations
A good recommendation engine can make a difference not just for Netflix, but for any
online business. This is because there are two fundamental activities online - Search and Browse. When a consumer knows exactly what she is looking
for, she searches for it. But when she is not looking for anything specific, she browses.
It is the browsing that holds the golden opportunity for a recommendation system, because
the user is not focused on finding a specific thing - she is open to suggestions. During browsing, the user’s attention (and their money) is up for grabs. By showing
the user something compelling, a web site maximizes the likelihood of a transaction. So
if a web site can increase the chances of giving users good recommendations, it makes
more money. Obviously this is a difficult problem, but the incentive to solve it is very
big. The main approaches fall into the following categories:
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 thing itself
A combination of the three approaches above
We will now explore these different approaches by looking at old-timers like Amazon
and newbies like Pandora and del.icio.us.
I’ve spent the night yesterday to try to fix the CSS for myspace…. Please, don’t tell me that myspace is simple anymore !!! So here is my page (I’ve also tried the mybloglog widget).
I’ve talked about Zapruder before … the death of saddam is probably the first real video with as much historical sense as the death of Kennedy. So it’s done the video world has changed again.
I am a huge fan of Fraser’s blog Disruptive thoughts (a must read or a Hall of fame if you prefer). Over the last few days, he shared with it’s reader it’s songs best of for 2006. Here is the widget that sum up the all selection (U.[lik]
will add 1 suggestion every 4 songs, so if you want the exact selection go to his blog).
So here we are … a fresh new year. A Year with style … Her Majesty style …. 007 ! I wish to every people I shared, back in 2006, a chat with a lot of success. To all entrepreneurs …. friends …. and of course my familly… may 2007 be a bright year for you. Here is a video from Richard St John on how to achieve success (Thks Mapper) :
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