Latent Structured Ranking
by Sunny K. Ujjawal, Search Analyst at Search industry on Mar 30, 2013
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Many latent (factorized) models have been ...
Many latent (factorized) models have been
proposed for recommendation tasks like collaborative ﬁltering and for ranking tasks like
document or image retrieval and annotation.
Common to all those methods is that during inference the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the
ranked list (i.e. considering the set of items
returned as a whole) is not taken into account. This can be a problem because the
set of top predictions can be either too diverse (contain results that contradict each
other) or are not diverse enough
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