Posted on Mar 30, 09 11:33 PM PDT
Lunch calculates commonalities in taste and opinions among the site's members and provides similarity-driven results, filtering out the noise and instead, focusing on information that is most relevant to each individual. Lunch uses what the company terms "proprietary Similarity Engine". To drive the engine, Lunch relies on passionate users who want to share their opinions, ratings, reviews, and discussions with others.
Combining the best of Wiki (facts) and social data points (opinions), in a social networking platform wrapper, Lunch seems like a personalized Yelp! experience across any topic of interest. As users interact with data points on Lunch, they get to see how others responded to the same data points, who else is in their response-based network, etc.—essentially continuously filtering and expanding the information presented to users based user actions.
Lunch was founded in 2008 by J.R. Johnson who sold his previous companies: VirtualTourist.com and OneTime.com to Expedia. Johnson brings a wealth of experience in monetizing targeted-data environment through advertisements. While ad revenue models have been under attack this past year—and rightfully so—Lunch could be a true delight for ad publishers given its highly contextual nature. Lunch, in essence, has created a multi-layered filtering for ad publishers—going from extremely broad to thinly woven sieve—guaranteeing extremely high relevance with much greater chance of return.
We found the concept of Lunch interesting both for users who wish to explore new and relevant content specific to them and to ad publishers. Beware, however, that if you wish for truly personalized content, you will need to (want … more