학술논문
Cumulative Citation Recommendation: A Feature-Aware Comparison of Approaches
Document Type
Conference
Author
Source
2014 25th International Workshop on Database and Expert Systems Applications Database and Expert Systems Applications (DEXA), 2014 25th International Workshop on. :193-197 Sep, 2014
Subject
Language
ISSN
1529-4188
2378-3915
2378-3915
Abstract
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.