Principal AI Scientist
Richard is a principal data scientist at Target responsible for query understanding and ranking in search. Before joining Target, Richard was a staff architect and senior research scientist at Baidu USA, where he worked on knowledge search, virtual health assistant, and speech recognition systems. When he was a software engineer at Google, he contributed to several projects such as Google Squared and Knowledge Graph. Richard received his Ph.D. from Carnegie Mellon where his primary focus was on NLP, machine learning, and information retrieval/extraction.
Richard Wang is speaking at the following session/s
Using Deep Learning and Customized Solr Components to Improve Search Relevancy at Target
Target uses a combination of deep learning models and custom Solr components to deliver highly accurate search results at scale. This talk will give an overview of the various convolutional neural network (CNN)-based classification models used to identify different search intent and attributes. It will also give a description of the type of data that these models are built on, how they are trained, and the quality of predictions they produce.
The talk will also present details about the various custom Solr components used to combine the deep learning signals. These include custom post filters used to control result set recall and custom scorers for combining different signals using a weighted approach. All the custom components have been designed to work at a high scale, and so this talk will also focus on performance considerations.
Gain insights into the state-of-the-art deep learning algorithms being used to power e-commerce search at Target and how to customize Solr to blend multiple ML signals at a large scale.
Anyone looking to use ML techniques for search relevancy (e-commerce or other and basic ML background would be helpful) and anyone looking to customize the internals of Solr and write own components (working knowledge of Solr required)