Customer-obsessed science

This paper describes how to define and execute tasks that depend on localization for their success. Real-world robotic systems that perform precise work at endpoints generally have tasks that fail if a robot’s localization error is above the task requirement. However, most systems for considering localization error are task-agnostic. We distinguish between business-case-required work tasks and localization

Product search for online shopping should be season-aware, i.e., presenting seasonally relevant products to customers. In this paper, we propose a simple yet effective solution to improve seasonal relevance in product search by incorporating seasonality into language models for semantic matching. We first identify seasonal queries and products by analyzing implicit seasonal contexts through time-series

In order to determine the relevance of a given item to a query, most modern search ranking systems make use of features which aggregate prior user behavior for that item and query (e.g. click rate). For practical reasons, when running A/B tests on ranking systems, these features are generally shared between all treatments. For the most common experiment designs, which randomize traffic by user or by session

Online evaluation techniques are widely adopted by industrial search engines to determine which ranking models perform better under a certain business metric. However, online evaluation can only evaluate a small number of rankers and people resort to offline evaluation to select rankers that are likely to yield good online performance. To use offline metrics for effective model selection, a major challenge

The Transformer Memory as a Differentiable Search Index (DSI) has been proposed as a new information retrieval paradigm, which aims to address the limitations of dual-encoder retrieval framework based on the similarity score. The DSI framework outperforms strong baselines by directly generating relevant document identifiers from queries without relying on an explicit index. The memorization power of DSI