Sunil Srinivasan

Paul Anderson photo

Lead Engineer
Target

Sunil is a Lead Engineer at Target with 19+ years of experience. He is responsible for leading the Search Platform initiatives and enabling integration with Query Understanding and Ranking components. Sunil has experience in designing large scale performant Search systems using Solr. He has previously worked for AOL Mail building search using Solr. He has a Computer Science engineering degree from Bangalore University.

Sunil Srinivasan is speaking at the following session/s

Using Deep Learning and Customized Solr Components to Improve Search Relevancy at Target

Thursday | 11:22PM - 11:22PM |

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.

Attendee Takeaway
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.

Intended Audience
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)

 

Level:
All Levels