Sinergia: Improving Browsing By Exploiting Community Knowledge

Finding useful information in the web using today’s search engine technology is still a time consuming, and often futile, task. This is not just because of the overwhelming number of results returned but also because of the uncustomized, one-size-fits-all results which do not care about the context and ability of the client. We propose to improve the situation by aggregating knowledge of what users with common preferences find useful while doing similar search queries. This knowledge can then be leveraged to provide refined results using that information. Furthermore, we extend the idea to present users with a customized list of pages related to their current page. We also use the concept of letting users rank different pages and clustering users based on these rankings. In this paper, we describe the motivation, design and implementation of an application to demonstrate proof-of-concept. We also discuss the general issues of scalability, portability and availabity etc. for this application