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Re-ranking Based Diversification: A Unifying View
June 26, 2019 Β· Declared Dead Β· π International Conference on the Theory of Information Retrieval
"No code URL or promise found in abstract"
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Authors
Shameem A Puthiya Parambath
arXiv ID
1906.11285
Category
cs.IR: Information Retrieval
Cross-listed
stat.ML
Citations
1
Venue
International Conference on the Theory of Information Retrieval
Last Checked
4 months ago
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.
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