| San Francisco, California, United States|
Francis Kelly, Ken Fromm, David Marks
Loomia was an Internet technology company based in San Francisco, California in the United States. Loomia offers a module that recommends content on a Web site. The company is part of a growing Internet trend that aims to bridge the gap between technological capabilities and user intents. Loomia's technology analyzes the content on the Web site, as well as user behaviors and social contexts to offer additional content that reflects the user's interests. The technology is reported to find the overlap between these diverse datasets as well as matching it against publisher content preferences. In doing so, Loomia's technology attempts to surface publishers' most valuable content and recommend articles and videos that matter to users.
Loomia has worked with major media companies to optimize their Web site content such as Time.com, The Wall Street Journal, Forbes, CNET, US News & World Report, and others. The content recommendation module can be found on these sites under the header of "People Who Read This Also Read."
Loomia was founded by David Marks, Ken Fromm, and Francis Kelly in 2005.
The company is located in San Francisco, CA.
2005 – Loomia is founded in San Francisco, CA
June 2005 – Loomia launches its videocasting and podcasting technology.
January 2006 – Loomia launches its content recommendation technology.
September 2006 – Loomia launches on Audible.com recommending paid content.
March 2007 – Loomia acknowledged in Cool Vendors in Media, 2007 by Gartner.
July 2007 – Loomia partners with The Wall Street Journal.
January 2008 – Loomia launches SeenThis? Facebook application.
April 2008 – Loomia raises $5 million in Series A funding from Asset Management Company, Teléfonica Capital, and Peacock Equity.
January 2009 – Loomia soft launches Targeted Content Discovery and Video Discovery products.
January 2009 – Loomia is a 2009 OnMedia 100 winner.
Loomia has ceased operations.
Founded in 2005, Loomia garnered funding from a number of angel investors in Silicon Valley. Loomia launched its product in June of that year. Originally, the company offered podcasting recommendations on loomia.com. People could subscribe to podcasts, listen to them onsite, or download them. The recommendation engine that was part of the Web site gave users recommended podcasts based on the individual's ratings and the ratings of other community members. However, Loomia soon saw other opportunities for their recommendation engine, and Loomia began using their technology to suggest new Web pages to users on content and ecommerce sites While big sites like Amazon have the capital to develop their own recommendation technology, many other publishers don't have the budget for such development. Others prefer to outsource or use existing technologies. Subsequently, the company moved into a waiting niche for recommendation technology. By 2007, the company had partnered with The Wall Street Journal, solidifying their presence in the space.
Initially, Loomia's recommendation technology used a collaborative filtering approach. However, as the company continued to innovate, content analysis was added sometime in 2006. At this point, the company was not necessarily using content analysis in conjunction with collaborative filtering.
In January 2008, Loomia added in a social context to improve the content recommendations. One of the products Loomia launched at this time was the SeenThis? application. It shows readers which articles and videos are popular within their specific social networks, predominantly through a Facebook app. Content comes from Loomia's clients (i.e.WSJ.com, CNET, and NBC.com), but the popularity amongst the social groups determines how widespread the content becomes.
By October 2008, Loomia blended all the inputs to find the best recommendations amidst all of the criteria, and this led to a soft launch of Targeted Content Discovery and Video Discovery products in January 2009.
Loomia's content recommendation technology is built upon multiple Open Source technologies including Python, MySQL, Apache, and CentOS. The code base is mostly Python with portions in C. Loomia’s Targeted Content Discovery engine analyzes visitor behavior, textual and metadata, and social data to create recommendations that appear in an onsite module. As new content items are added, they are included in the system's calculations. For example, on The Wall Street Journal, the module suggests other WSJ content based on what the user has read previously on the site and compared to what other users have read. Loomia's Targeted Content Promotion feature within the recommendation engine is designed to help online publishers optimize their Web sites so that they can make more money off of their content. Loomia had experimented with TCP mapping to find out what sections of Web sites are the best revenue drivers, and the company created this targeting feature in November 2008. It would be packaged into the Video Discovery and Targeted Content Discovery products that Loomia launched in early 2009
A number of other technology companies are working to carve out a niche in the recommendation technology space. One such competitor is Aggregate Knowledge, which has raised a total of $25 million in venture capital to date.