In this work, we conduct a feature-aware comparison of approaches to Cumulative Citation Recommendation (CCR), a task that aims to filter and rank a stream of documents according to their relevance to entities in a knowledge base. We conducted experiments starting with a big feature set, identified a powerful subset and applied it to comparing classification and learning to rank algorithms. With few set of powerful features, we achieve better performance than the state-of-the-art. Surprisingly, our findings challenge the previously known preference of learning-to-rank over classification: in our study, the CCR performance of the classification approach outperforms that using learning-to-rank. This indicates that comparing two approaches is problematic due to the interplay between the approaches themselves and the feature sets one chooses to use.
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M. Spies , R.R. Wagner , L. Lhotsk√° , H. Decker , S. Link
COMMIT: Infinity (P01)
International Workshop on Text-based Information Retrieval
Human-centered Data Analysis

Gebremeskel, G.G, He, J, de Vries, A.P, & Lin, J.J.P. (2014). Cumulative Citation Recommendation: A Feature-aware Comparisons of Approaches. In M Spies, R.R Wagner, L Lhotsk√°, H Decker, & S Link (Eds.), . doi:10.1109/DEXA.2014.49