Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis

In this paper, we analyze re-ranking based recommendation diversification algorithms and observe that, commonly, such algorithms can be unified under the scheme of maximizing submodular or modular objective functions from the class of parameterized concave over modular functions. We showcase that such diversification objective functions can be expressed in a generic functional form consisting of the relevance and diversity terms. We then theoretically analyze and show that the total curvature of submodular functions provides insights about the relevance-diversity trade off. This is expected to support data analysts to seek balanced hyperparameter values and, thus, serve as a 'vehicle of validation' by checking the total curvature of submodular objective functions. Our experimental evaluation and performance assessment over benchmark datasets are aligned with our theoretical analysis. We also discuss the importance of balanced trade-off between relevance and diversity in specific application settings like news recommendations to trade-off algorithmic bias and short term user engagement