Probabilistic latent maximal marginal relevance

@inproceedings{Guo2010ProbabilisticLM,
  title={Probabilistic latent maximal marginal relevance},
  author={Shengbo Guo and Scott Sanner},
  booktitle={SIGIR},
  year={2010}
}
Diversity has been heavily motivated in the information retrieval literature as an objective criterion for result sets in search and recommender systems. Perhaps one of the most well-known and most used algorithms for result set diversification is that of Maximal Marginal Relevance (MMR). In this paper, we show that while MMR is somewhat ad-hoc and motivated from a purely pragmatic perspective, we can derive a more principled variant via probabilistic inference in a latent variable graphical… CONTINUE READING
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