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
- 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. :-)