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CoFeed: privacy-preserving Web search recommendation based on collaborative aggregation of interest feedback

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2011
Author Felber Pascal, Kropf Peter, Leonini Lorenzo, Luu Toan, Rajman Martin, Rivière Etienne, Schiavoni Valerio, Valerio José,
Project MistNet: An Experimental Peer-to-peer Platform for the Cloud
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Original article (peer-reviewed)

Journal Software Practice and Experience
Page(s) 1
Title of proceedings Software Practice and Experience
DOI 10.1002/spe.1127


Search engines essentially rely on the structure of the graph of hyperlinks. While accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complementary results that are tailored to the user’s expectations. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and that considers feedback to build user- and document-centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user’s interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Moreover, collecting the recommendation data poses the problem of users’ privacy, and the bias one peer can induce to the system by sending fake recommendations. To that end, CoFeed ensures both publisher anonymity and rate limitation. With the former, the origin of the data is never known by the server that processes it, even if several servers collude to spy on some user. The latter, combined with decoupled authentication, allows to minimize the influence of cheating peers sending fake recommendations. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balancing.