Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Multimodal Representation Learning for Click-Through Rate
  Prediction   For better user experience and business effectiveness, Click-Through Rate
(CTR) prediction has been one of the most important tasks in E-commerce.
Although extensive CTR prediction models have been proposed, learning good
representation of items from multimodal features is still less investigated,
considering an item in E-commerce usually contains multiple heterogeneous
modalities. Previous works either concatenate the multiple modality features,
that is equivalent to giving a fixed importance weight to each modality; or
learn dynamic weights of different modalities for different items through
technique like attention mechanism. However, a problem is that there usually
exists common redundant information across multiple modalities. The dynamic
weights of different modalities computed by using the redundant information may
not correctly reflect the different importance of each modality. To address
this, we explore the complementarity and redundancy of modalities by
considering modality-specific and modality-invariant features differently. We
propose a novel Multimodal Adversarial Representation Network (MARN) for the
CTR prediction task. A multimodal attention network first calculates the
weights of multiple modalities for each item according to its modality-specific
features. Then a multimodal adversarial network learns modality-invariant
representations where a double-discriminators strategy is introduced. Finally,
we achieve the multimodal item representations by combining both
modality-specific and modality-invariant representations. We conduct extensive
experiments on both public and industrial datasets, and the proposed method
consistently achieves remarkable improvements to the state-of-the-art methods.
Moreover, the approach has been deployed in an operational E-commerce system
and online A/B testing further demonstrates the effectiveness.

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The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th