Re-ranking Based Diversification: A Unifying View
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arXiv
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866909153171079168 |
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| author | Parambath, Shameem A Puthiya |
| author_facet | Parambath, Shameem A Puthiya |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1906_11285 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Re-ranking Based Diversification: A Unifying View Parambath, Shameem A Puthiya Information Retrieval Machine Learning 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. |
| title | Re-ranking Based Diversification: A Unifying View |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/1906.11285 |