Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Greedy MAP Inference for Determinantal Point Process to Improve
  Recommendation Diversity   The determinantal point process (DPP) is an elegant probabilistic model of
repulsion with applications in various machine learning tasks including
summarization and search. However, the maximum a posteriori (MAP) inference for
DPP which plays an important role in many applications is NP-hard, and even the
popular greedy algorithm can still be too computationally expensive to be used
in large-scale real-time scenarios. To overcome the computational challenge, in
this paper, we propose a novel algorithm to greatly accelerate the greedy MAP
inference for DPP. In addition, our algorithm also adapts to scenarios where
the repulsion is only required among nearby few items in the result sequence.
We apply the proposed algorithm to generate relevant and diverse
recommendations. Experimental results show that our proposed algorithm is
significantly faster than state-of-the-art competitors, and provides a better
relevance-diversity trade-off on several public datasets, which is also
confirmed in an online A/B test.

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The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci