Customer-obsessed science
Research areas
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November 28, 20254 min readLarge language models are increasing the accuracy, reliability, and consistency of the product catalogue at scale.
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November 20, 20254 min read
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October 20, 20254 min read
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October 14, 20257 min read
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October 2, 20253 min read
Featured news
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KDD 20222022In this paper, we introduce Shop the Look, a web-scale fashion and home product visual search system deployed at Amazon. Building such a system poses great challenges to both science and engineering practices. We leverage large-scale image data from the Amazon product catalog and adopt effective strategies to reduce the human effort required to annotate data. By employing state-of-the-art computer vision
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Interspeech 20222022We present an automatic reading evaluator that listens to novice young readers and offers feedback based on the reading accuracy. In order to not discourage the reader, the model should not misrecognize correctly read tokens (false rejects), which may come at the expense of tolerating some reading mistakes (false accepts). To minimize the former, we explore two approaches to provide reference text – the
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ICML 20222022Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex responses can be modelled whilst retaining interpretability. Previous work showed that additive Gaussian process models require high-dimensional interaction terms. We propose the orthogonal additive kernel (OAK), which imposes an orthogonality
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Interspeech 20222022Entity Resolution (ER) in spoken dialog systems can suffer from phonetic variation in search queries caused by Automatic Speech Recognition (ASR) errors. In this paper, we propose a phonetic embedding technique to improve the robustness of the ER system to this variation, which includes a phonetic embedding model, a training-data augmentation and sampling method, and an ASR robustness evaluation methodology
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ICML 20222022Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of users for recommended products and their categories. To maximize statistical efficiency, it is important to leverage these correlations when learning. We formulate a bandit variant of this problem where the correlations of mean action rewards are represented by a hierarchical
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