Diversification is a method of improving user satisfaction by increasing the variety of information shown to user. Due to the lack of a precise definition of information variety, many diversification techniques have been proposed. These techniques, however, have been rarely compared and analyzed under the same setting, rendering a ‘right’ choice for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of diversification. Specifically, we integrate several state-of-the-art diversification algorithms in a comparable manner, and measure distinct characteristics of these algorithms with various settings. We then provide in-depth analysis of the benchmark results, obtained by using both real data and synthetic data. We believe that the findings from the benchmark will serve as a practical guideline for potential applications.
CITATION STYLE
Thang, D. C., Tam, N. T., Hung, N. Q. V., & Aberer, K. (2015). An evaluation of diversification techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9262, pp. 215–231). Springer Verlag. https://doi.org/10.1007/978-3-319-22852-5_19
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