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Re-ranking Based Diversification: A Unifying View

Shameem A Puthiya Parambath

Abstract

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.

Re-ranking Based Diversification: A Unifying View

Abstract

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.

Paper Structure

This paper contains 8 sections, 4 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diversification Algorithms
  4. Diversity-Relevance Trade-off
  5. Experiments
  6. Evaluation Metrics
  7. Results & Discussion
  8. Conclusion

Figures (1)

  • Figure 1: Relevance-Diversity trade-off as a function of recommendation size for different values of total curvature ($\alpha$)