Ranking of evolving stories through meta-aggregation
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In this paper we focus on the problem of ranking news stories within their historical context by exploiting their content similarity. We observe that news stories evolve and thus have to be ranked in a time and query dependent manner. We do this in two steps. First, the mining step discovers metastories, which constitute meaningful groups of similar stories that occur at arbitrary points in time. Second, the ranking step uses well known measures of content similarity to construct implicit links among all metastories, and uses them to rank those metastories that overlap the time interval provided in a user query. We use real data from conventional and social media sources (weblogs) to study the impact of different meta-aggregation techniques and similarity measures in the final ranking. We evaluate the framework using both objective and subjective criteria, and discuss the selection of clustering method and similarity measure that lead to the best ranking results.
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Jannach D; Zanker M; Ge M; Gröning M (Springer Berlin Heidelberg, 2012)The paper reviews and classifies recent research in recommender systems both in the field of Computer Science and Information Systems. The goal of this work is to identify existing trends, open issues and possible directions ...
Byrom, R; Coghlan, B; Cooke, A; Cordenonsi, R; Cornwall, L; Craig, M; Djaoui, A; Duncan, A; Fisher, S; Gray, A; Hicks, S; Kenny, S; Leake, J; Lyttleton, O; Magowan, J; Middleton, R; Nutt, W; O'Callaghan, D; Podhorszki, N; Taylor, P; Walk, J; Wilson, A (Springer, 2005)R-GMA (Relational Grid Monitoring Architecture)  is a grid monitoring and information system that provides a global view of data distributed across a grid system. R-GMA creates the impression of a single centralised ...