MAAY: a decentralized personalized search system

This paper focuses on decentralized personalized search engines. It is composed of three parts. Firstly, we formulate the problem and we propose a graph-based measure of quality of a document given a user and a word. This quality gives a formal measure to test the personalized relevance of answers provided by search engines. Secondly, we present a decentralized system namely MAAY which aims to provide personalized results to its users. Users can share, search and retrieve documents from others. Nodes locally learn others´ profile from previous interactions and use feedbacks to raise words characterizing a document. By querying preferentially nodes with similar profile and by ranking documents according to the requester profile, nodes may find documents which are relevant for them. Finally, we propose a framework for experimental studies of such system. Then, we use it to show that the main principles of MAAY could benefits to users