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arXiv · 1207.6600

Diversity in Ranking using Negative Reinforcement

Rama Badrinath·C. E. Veni Madhavan
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Abstract

In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method which works in an iterative fashion based on random walks to achieve diversity. Specifically, we use negative reinforcement as a main tool to introduce diversity in the Personalized PageRank framework. Experiments on two benchmark datasets show that our algorithm is competitive to the existing methods.

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Rama Badrinath, C. E. Veni Madhavan. 2012-07-27. Diversity in Ranking using Negative Reinforcement. https://arxiv.org/abs/1207.6600

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