Macrovision Acquires AMG

Breaking news.... Macrovision acquires AMG (aka All Media Guide). AMG is most widely known for their music database products (which powers many media players and music stores), and also has a portfolio that include recommendation engines (Tapestry), music recognition technology (LASSO), and their consumer sites AllMusic, AllMovie and AllGame.

paidContent.org - The Economics of Content - Financial: "The acquisition of AMG extends Macrovision’s solutions for the enhancement and distribution of digital content by making it easier for consumers to discover, purchase, interact with and play back digital media assets, which is a strategic priority for Macrovision. Macrovision and AMG’s combined solutions offer consumer electronics device manufacturers the ability to better manage media while putting information about millions of movies, videos and songs within the easy reach of consumers. AMG’s comprehensive data is also intended to strongly appeal to Macrovision video content customers, which include Hollywood studios. AMG’s content enables those customers to more easily monetize digital media by making content identification, navigation, recommendation and enjoyment simpler for consumers. “In an era when consumers have many choices, cell phone manufacturers, electronics device makers and service providers want to make video and all other entertainment content quick and easy to find,” said Fred Amoroso, CEO of Macrovision Corporation. “AMG plays a critical role in enhancing the overall consumers’ user experience in searching, discovering, purchasing and enjoying entertainment media. Its offerings complement our existing solhttp://www.blogger.com/img/gl.link.gifutions and partnerships for helping content owners monetize their digital media assets and device makers retain and grow their customer base.”

“Metadata is a critical component of media commerce and demand for rich descriptive data such as reviews, album art, artist biographies and accompanying editorials is growing quickly,” said Karl Ryser Jr., President of AMG. “It takes years to accumulate a high-quality database of metadata, and AMG has become an essential partner for its customers. Joining together with Macrovision puts us in an even better position to help more consumer electronics manufacturers, retailers and content owners collaborate with customers to create outstanding online media experiences.”

UPDATE: For those curious, the reports say the deal was for $82 million.

RecSys 2007

I spent a few days in Minneapolis last week attending the Recommender Systems Conference. The conference is in its 2nd year and brings together academics and industry to discuss the future, challenges and opportunities for recommender systems. This year there are about 110 participants from 16 countries, including industry representation from Google, Netflix, Amazon, AMG, Digg, AOL, eBay, Unilever, Aggregate Knowledge and MyStrands.

Day 1 featured a very interesting keynote speech from Khrishna Bharat, Principal Scientist from Google, about the history and future of news journalism and the social responsibility we all share in ensuring the continued freedom of speech. He also touched on the process by which Google crawls, clusters, ranks, classifies the most relevant stories in Google News. Followed by some insight into the increased user engagement they were able to realize with the introduction of their personalized news stories. The clickthrough of personalized news stories is indeed higher than on just a blind list of "top stories".

The keynote was followed by a number of academic papers presentations - focused on the hot topics of privacy and trust in collaborative filtering engines. Indeed some very interesting research going on in these fields, and I look forward to seeing what the continued research here bears out in the coming months and years.

After lunch, I was honored to take part in a panel with the focus of "Where should we be investing most in research and practice to increase the value of recommenders?". This was the opportunity for the industry folks like ourselves to provide some insight to the academics about the "real world" issues that we are trying to solve or improve. It was a lively discussion that extended the dialog on recommenders beyond the science and into user experience, consumer value and business models built around them. The panel included:

• Joaquin Delgado, CTO, Lending Club Corp.


• Jason Herskowitz, VP of Consumer Products, MyStrands


• Kartik Hosanagar, Assistant Professor, Wharton School of Business, University of Pennsylvania


• David Jennings, DJ Alchemi LLC


• Zac Johnson, Product Manager, All Media Guide, Inc.

The day closed out with Poster Sessions by the academic community and some very interesting demos, with the lively discussion moving on to dinner and drinks.

The second day presented us with more research papers and another industry session titled "Appraising Recommender Systems" featuring:

• Jennifer Consalvo, Director of Personalization, AOL

• Greg Linden, Founder, Findory, Inc.

• Shail Patel, Platform Leader, Unilever Corporate Research

• Neel Sundaresan, Director, eBay Research Labs

• Tim Vogel, Chief Scientist, Aggregate Knowledge, Inc

All-in-all, the industry folks (myself included) challenged the academics with problems and questions.... not answers. Some of the ones I found more interesting were:

• How conservative should a "good" recommendation be? The pro is the con, in that a conservative recommendation is rarely wrong, but also just as rarely leads to a serendipitous discovery.
• When is a recommendation "good enough"? Where is the point of diminishing returns in further research into the algorithms?
• How do you differentiate based on algorithm? Is it possible, or do companies need to focus on differentiating the experience they present *around* the algorithm?
• Do consumers even want the "best" recommendation, or just the most useful? Greg Linden suggested that if Amazon just recommended Harry Potter to every customer, that would probably be the *best*, but not nearly as useful the consumer as recommending something less obvious.
• How do you present a "story" around a recommendation that makes it interesting enough for a user to invest in?
• Can the industry get behind a standard "taste data" format that enables users to own their preferences and consumption history and seamless share that information with any site they desire without having to train yet another system?

The side-benefit of this trip is that I got to meet a number of "Facebook Friends" in person for the first time - David Jennings, Paul Lamere, Zac Johnson, Oscar Celma and others from the "music 2.0" community. Sorry about the tequila shots guys... not my idea. :-)