The Internet is filled with content that is growing by millions of pages a day. As the information on the Internet grows exponentially, it has becomes harder and harder to find personalized information. These days, getting the "desired" results from a search engine has become an art - users can no longer simply type a one or two word query and get the results they are looking for. The users must refine or expand their query to find the results that meet their needs.

In early 2004, the founders of Filangy developed an algorithm to better target and optimize advertisements shown on web pages. This algorithm did a remarkable job of targeting advertisements not only by matching keywords, but also by gaining some understanding about the user. The next logical step was to expand this technology so that it could be applied to the Internet, thereby allowing users to personalize their searches and get the results that they are most likely to be interested in.

The first step in creating a personalized search experience is to get an understanding of the user's interests. Moreover, to make this understanding universal, we had to do it in a way that overcame language barriers - so that a user in China, Japan or Morocco has the same user experience irrespective of which language they type their search query in. Another important step in getting users the desired information was to create a system to distinguish good information from the not-so-good information. While content and link based analysis are good measures for removing bad pages, nothing is better than having users collectively decide which page is good or bad. This collective usage analysis is the cornerstone of the Filangy ActiveWeb.