Re-ranking Based Diversification: A Unifying View
This work provides a theoretical unification for diversification algorithms, which is incremental as it refines existing methods without introducing new paradigms.
The paper analyzes re-ranking algorithms for diversification, showing that most maximize submodular/modular functions and linking hyperparameter tuning to adjusting the 'total curvature' for relevance-diversity trade-offs.
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adjusting the hyperparameter of the concave/linear composition to trade-off relevance and diversity, if any, one is in fact tuning the `total curvature' of the function for relevance-diversity trade-off.