Customer-obsessed science

We present Catalog Phrase Grounding (CPG), a model that can associate product textual data (title, brands) into corresponding regions of product images (isolated product region, brand logo region) for e-commerce vision-language applications. We use a state-of-the-art modulated multimodal transformer encoder-decoder architecture unifying object detection and phrase-grounding. We train the model in self-supervised

E-commerce stores face the challenge of missing and inconsistent attribute values in the product detail pages and have to impute them on behalf of their vendors. Traditional approaches formulate the problem of attribute extraction (AE) from product profiles as natural language tasks such as information extraction or text classification. Such models typically operate at high precision but may yield low recall

The modern e-commerce web pages have brought better customer experience and more profitable services by whole page optimization at different granularity, e.g., page layout optimization, item ranking optimization, etc. Generating the proper page layout per customer’s request is one of the vital tasks during the web page rendering process, which can directly impact customers’ shopping experience and their

Twitch is a community driven live streaming video service which uses recommendations trained on implicit feedback data. While this data is available in large quantities, it is subject to various biases and shortcomings. In particular, Twitch watch history data is heavily affected by the unpredictable and irregular behavior of users and streams going online and offline. Two resulting issues are: accounting

We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed