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eldavojohn writes "As we discussed at the time, there was a strange development at the end of Netflix's competition in which The Ensemble passed BellKor's Pragmatic Chaos by 0.01% a mere twenty minutes after BellKor had submitted results past the ten percent mark required to win the million dollars. Unfortunately for The Ensemble, BellKor was declared the victor this morning because of that twenty-minute margin. For those of you following the story, The New York Times reports on how teams merged to form Bellkor's Pragmatic Chaos and take the lead, which sparked an arms race of teams conjoining to merge their algorithms to produce better results. Now the Netflix Prize 2 competition has been announced." The Times blog quotes Greg McAlpin, a software consultant and a leader of the Ensemble: "Having these big collaborations may be great for innovation, but it's very, very difficult. Out of thousands, you have only two that succeeded. The big lesson for me was that most of those collaborations don't work."

John Snodgrass writes "Neil Hunt, Chief Product Officer at Netflix, has announced on the Netflix Prize Forums that they are planning to hold a new data mining competition. The second competition will have some twists and is expected to be shorter in duration. It will feature two grand prizes, to be awarded in a 6 and 18 month time frame. A previous competitor still active on the board has already dubbed it: 'The Sparse Matrix: Reordered' and 'The Sparse Matrix: Factorizations.'"

suraj.sun updates us on the Netflix Prize now that the competition has officially closed. We discussed the new leader with one day to go in the contest: The Ensemble, taking the lead from long-time leader BellKor's Pragmatic Chaos, the first contestant to submit an entry that broke the 10% barrier. In the contest's final day, BellKor re-took the lead with 20 minutes to go, then The Ensemble apparently pulled a Michael Phelps with 4 minutes to go, squeaking ahead by 0.01%. At least so the leaderboard claims — but those numbers are posted by the competing teams. The NY Times reports that an official winner will not be named until September — Netflix needs that much time to pore through the complex entries and read the code. Netflix contacted BellKor on Sunday to tell them the team remained in first place; The Ensemble has had no such notification.

brajesh writes "The Netflix Prize, an algorithm competition to improve the Netflix Cinematch recommendation system by more than 10%, has a new leader — The Ensemble — just one day before the competition ends. The 30-day race to the end was kicked off after BellKor's Pragmatic Chaos submitted the first entry to break the 10% barrier, with the results showing a 10.08% improvement. The Ensemble, made up of three teams who chose to join forces ('Grand Prize Team,' 'Opera Solutions' and 'Vandelay United), has managed to overtake BellKor with a score of 10.09% — an improvement of .01% over the former leaders. From the article on Techcrunch: 'The competition will end [today], so teams still have a little bit of time left to make their last-second submissions, but things are looking good for The Ensemble. This has to be absolutely brutal for team BellKor.'"

MadAnalyst writes "The long-running $1,000,000 competition to improve on the Netflix Cinematch recommendation system by 10% (in terms of the RMSE) may have finally been won. Recent results show a 10.05% improvement from the team called BellKor's Pragmatic Chaos, a merger between some of the teams who were getting close to the contest's goal. We've discussed this competition in the past."

Joe Kauzlarich writes "In 2006, the on-line movie rental store Netflix proposed a $1 million prize to whomever could write a movie recommendation algorithm that offered a ten percent improvement over their own. As of this writing, the intriguingly-named Gravity and Dinosaurs team holds first place by a slim margin of .07 percent over BellKor, their algorithm an 8.82 percent improvement on the Netflix benchmark. So, the question remains, how do they write these so-called recommendation algorithms? A new O'Reilly book gives us a thorough introduction to the basics of this and similar lucrative sciences." Keep reading for the rest of Joe's review. Programming Collective Intelligence author Toby Segaran pages 334 publisher O'Reilly Media Inc. rating 9/10 reviewer Joe Kauzlarich ISBN 9780596529321 summary Introduction to data mining algorithms and techniques Among the chief ideological mandates of the Church of Web 2.0 is that users need not click around to locate information when that information can be brought to the users. This is achieved by leveraging 'collective intelligence,' that is, in terms of recommendations systems, by computationally analyzing statistical patterns of past users to make as-accurate-as-possible guesses about the desires of present users. Amazon, Google and certainly many other organizations, in addition to Netflix, have successfully edged out more traditional competitors on this basis, the latter failing to pay attention to the shopping patterns of users and forcing customers to locate products in a trial and error manner as they would in, say, a Costco. As a further illustration, if I go to the movie shelf at Best Buy, and look under 'R' for Rambo, no one's going to come up to me and say that the Die Hard Trilogy now has a special-edition release on DVD and is on sale. I'd have to accidentally pass the 'D' section and be looking in that direction in order to notice it. Amazon would immediately tell me, without bothering to mention that Gone With The Wind has a new special edition.

Programming Collective Intelligence is far more than a guide to building recommendation systems. Author Toby Segaran is not a commercial product vendor, but a director of software development for a computational biology firm, doing data-mining and algorithm design (so apparently there is more to these 'algorithms' than just their usefulness in recommending movies?). Segaran takes us on a friendly and detailed tour through the field's toolchest, covering the following topics in some depth:
Recommendation Systems
Discovering Groups
Searching and Ranking
Document Filtering
Decision Trees
Price Models
Genetic Programming
... and a lot more

As you can see, the subject matter stretches into the higher levels of mathematics and academia, but Segaran successfully keeps the book intelligible to most software developers and examples are written in the easy-to-follow Python language. Further chapters cover more advanced topics, like optimization techniques and many of the more complex algorithms are deferred to the appendix.

The third chapter of the book, 'Discovering Groups,' deserves some explanation and may enlighten you as to how the book may be of some use in day-to-day software designs. Suppose you have a collection of data that is interrelated by a 'JOIN' in two sets of data. For example, certain customers may spend more time browsing certain subsets of movies. 'Discovering Groups' refers to the computational process of recognizing these patterns and sectioning data into groups. In terms of music or movies, these groups would represent genres. The marketing team may thus become aware that jazz enthusiasts buy more music at sale prices than do listeners of contemporary rock, or that listeners of late-60's jazz also listen to 70's prog, or similar such trends.

Certainly the applications of such tools as Programming Collective Intelligence provides us are broader than my imagination can handle. Insurance companies, airlines and banks are all part of massive industries that rely on precise knowledge of consumer trends and can certainly make use of the data-mining knowledge introduced in this book.

I have no major complaints about the book, particularly because it fills a gap in popular knowledge with no precursor of which I'm aware. Presentation-wise, even though Python is easy to read, pseudo-code is more timeless and even easier to read. You can't cut & paste from a paper book into a Python interpreter anyway. It may 've been more appropriate to use pseudo-code in print and keep the example code on the website (I'm sure it's there anyway).

If you ever find yourself browsing or referencing your algorithms text from college or even seriously studying algorithms for fun or profit, then I would highly recommend this book depending on your background in mathematics and computer science. That is, if you have a strong background in the academic study of related research, then you might look elsewhere, but this book, certainly suitable as an undergraduate text, is probably the best one for relative beginners that is going to be available for a long time.

You can purchase Programming Collective Intelligence from amazon.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.

An anonymous reader writes "An essay by a developer of recommendation systems makes a case for why so many people have trouble grasping Darwin's theory of evolution. Downplaying its conflict with religion, the essay suggests that evolution is in a specific class of "equilibrium seeking" concepts that tend to be extremely counterintuitive to most people. The hypothesis is supported by the observation that so many people reject the notion that evolution-like systems such as Wikipedia, prediction markets, and recommendation systems can actually be effective. Particularly fascinating is the description of his surprisingly simple algorithm for competing in the Netflix prize contest."

Baldrson writes "Within the first week of the announcement of The Netflix Prize a team has already beaten Netflix's own movie recommendation algorithm. This is pretty impressive given the previously quoted researcher who said: 'You're competing with 15 years of really smart people banging away at the problem.' The team is WXYZConsulting.com apparently registered by a data mining professor named Yi Zhang. Congratulations are in order for Netflix and Prof. Zhang's team who are demonstrating, yet again, the power of prizes to accelerate progress."

Baldrson writes "Within the first week of the announcement of The Netflix Prize a team has already beaten Netflix's own movie recommendation algorithm. This is pretty impressive given the previously quoted researcher who said: 'You're competing with 15 years of really smart people banging away at the problem.' The team is WXYZConsulting.com apparently registered by a data mining professor named Yi Zhang. Congratulations are in order for Netflix and Prof. Zhang's team who are demonstrating, yet again, the power of prizes to accelerate progress."