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55 similar to PeerSpective, as they are built from contributing users’ desktops [90]. Both Minerva [19] and YaCy [164] implement a peer-to-peer Web search engine without any points of centralization. Additionally, other projects [124,141] have examined replac- ing the centralized PageRank computation of Google with a decentralized approach. All of these projects, though, are primarily focused on replacing the functionality of existing centralized search engines with a decentralized architecture, rather than improving or personalizing Web search. A few systems have looked at personalizing responses to queries by taking a user’s preferences and interests into account when ranking pages. Most notably, A9 [1] and Google Personalized Search [61] allow users to create profiles to which search results are tailored. However, these systems require users to create detailed profiles to perform well, which represents a significant burden on users. There has also been much research into methods for automatically personalizing search queries by inferring user interests [68,150]. While these projects are concerned with personalization, the approach taken PeerSpective is complementary in that we obtain personalization by using social links to improve search results. The MAAY [100] project has examined combining both of the above approaches, providing a decentralized and personalized search engine. In ISpy [146], organizations deploy a single web proxy which records the results of past queries and uses these to influence future ones. Their approach is limited to the single social group consisting of an organization, in contrast, PeerSpective is able to use an arbitrary social networkPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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