Description

Design goal

Better result quality at lower costs while maintaining the simple handling and the speed of conventional search engines.

Architecture

Distributed crawling

When an user opens a page with the browser, it will be automatically inserted into the distributed index of the p2p network.
The additional network load and the site submission of a traditional crawler is omitted. Assuming a wide spread of FAROO this enables an almost complete index, updated in real time.

Distributed index

FAROO requires no central index server. Every FAROO user is part of a distributed index.
Every FAROO user is responsible for certain words. If an other FAROO user visits a web page, which contains one of these words, he stores the URL of the web page to the list of the assigned user.
There are always multiple users assigned to one word, in order to preserve the information, when a user leaves the network. Then an other FAROO-user takes his place.

Distributed ranking: PeerRank

PeerRank is a newly developed ranking algorithm. FAROO takes the user behavior when viewing a page into consideration for ranking. This is done automatically, without requiring a manual rating from the user. Thus for the first time the user as audience of a page also decides about its ranking. With previous ranking methods only the site owners determined the page rank, which were based on the linking of web pages among each other

Putting the ranking on a much wider base leads to a democratized, user centric ranking, while resistant against manipulation. For the first time the users decide themselves, which results are most important. In the respect this is a kind of social search, but omitting the existing drawbacks. Not only people known to the user are involved into the ranking, but all FAROO users. No registration is required, and no user profile or search history is stored at a central server.

Personalized ranking: PersonalRank

The personalized ranking of pages is based upon the areas of interest of the searching user. In order to determine the user focus are besides the visited web pages also the content of local documents analyzed.

If somebody is looking for a car and he has a pdf-brochure from VW on his desktop, then the car results, in which also the term VW occurs are higher ranked. If somebody has a lot of addresses from New York City within its documents and he is looking for a pizzeria, then the pizzerias from New York City are ranked higher.
This analysis is done at the users computer. No personal information is transferred outside from his computer. Besides this the user may at anytime disable analysis and personalization. At present no ranking is activated.