Exploiting tag information for search and personalization

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Unusual aggressive and rapidly growing glioblastoma multiforme: Case presentation

Glioblastoma multiform is one of the most rapidly progressing cerebral tumors and the most aggressive one in our neurosurgical experience. We present the case of a 45 year old patient with very aggressive type of tumor who had come to our service for the following: intense headache, confusion, right hemiparesis installed approximately one month before. IRM scan shows up the presence of a large tumoral mass without a precise border in the left temporal-parietal region which had extended all the way down to the thalamus. The planned intervention used 5-aminolevulinic acid (5-ALA) for the precise removal of the tumor mass, suboptimal because of the risk of lesioning the motor tracts – indicated by the intraoperative electrophysiological monitoring. After surgery the outcome was good with the partial regression of the motor deficit, but only after 3 weeks due to the unexpected tumor growth the neurological status started to decay and even worsened. The patient underwent surgery again with the partial remission of the symptoms although following imagistic controls showed up fast tumor growth once more. He was recommended to oncology service for the beginning of radiotherapy. We consider the evolution and invasion of this tumor in only a 3 weeks period being impressive

I know I stored it somewhere - Contextual Information and Ranking on our Desktop

This paper has explored two techniques - activity-based metadata and authority transfer annotations - as important contributions towards enabling efficient retrieval and ranking for the "personal digital repositories" building up on our computers. Activity-based metadata describe context information relevant for finding and connecting the resources we store on our desktop, authority transfer annotations help to rank retrieved resources in a personalized way. Global ranking services like Google or Citeseer-derived ranking services can initialize these personalized ranking measures. Our prototype uses the open source project Beagle as underlying desktop search infrastructure and extends its regular full-text indexing capabilities with contextual metadata and rankin

Semantically Rich Recommendations in Social Networks for Sharing, Exchanging and Ranking Semantic Context

Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures

Peer-Sensitive ObjectRank -- Valuing Contextual Information in Social Networks

Building on previous work on how to model contextual information for desktop search and how to implement semantically rich information exchange in social networks, we define a new algorithm, Peer-Sensitive ObjectRank for ranking resources on the desktop. The new algorithm takes into account different trust values for each peer, generalizing previous biasing PageRank algorithms. We investigat

Semantically Rich Recommendations in Social Networks for Sharing and Exchanging Semantic Context

Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures

Personalizing PageRank-Based Ranking over Distributed Collections

Abstract. In distributed work environments, where users are sharing and searching resources, ensuring an appropriate ranking at remote peers is a key problem. While this issue has been investigated for federated libraries, where the exchange of collection specific information suffices to enable homogeneous TFxIDF rankings across the participating collections, no solutions are known for PageRank-based ranking schemes, important for personalized retrieval on the desktop. Connected users share fulltext resources and metadata expressing information about them and connecting them. Based on which information is shared or private, we propose several algorithms for computing personalized PageRank-based rankings for these connected peers. We discuss which information is needed for the ranking computation and how Page-Rank values can be estimated in case of incomplete information. We analyze the performance of our algorithms through a set of experiments, and conclude with suggestions for choosing among these algorithms