Worum es bei uns geht

Regensburg is full of data science expertise, both in industry and in academia. Our aim is to bring together people who share an interest in this area and offer an environment for networking in an informal setting. We continue to have speakers with a range of backgrounds offering insights into a wide spectrum of data science ranging from enterprise search to music recommendation, from automatic fact-checking to avoiding harms and biases, from generative approaches to automatic question-answering. And that is not even everything. Other topics include large language models, industry use cases of natural language processing and the list goes on and on ... We have speakers from industry (e.g. Bloomberg, Netflix, Amazon, Spotify, Deloitte ...) and universities (CMU, Queen Mary, Essex, Regensburg ...). Want to present? Drop us a message. For more details on the organising team check: https://ai.ur.de/

Bevorstehende Events (1)

Daniel Wrigley: Exploring the Impact of Vector Search in E-Commerce

University of Regensburg

Dear all,

As unfortunate as it was that we had to postpone this week's meeting, here is some better news ... we have just lined up our next speaker! We are very happy to have Daniel Wrigley join us from OpenSource Connections. He follows a tradition of speakers looking at addressing very practical search-related problems in e-commerce settings (you will recall that we had Charlie Hull and René Kriegler both present at past Meetups). So make sure you sign up and keep the evening free ...

Looking forward to seeing you soon!
Udo, David and Bernd


Daniel Wrigley (OpenSource Connections)

Exploring the Impact of Vector Search in E-Commerce

With the advent of vector search engines, neural search frameworks and of course the availability of vector search capabilities in widely used search engines like Solr, Elasticsearch or Opensearch, the adoption of vectors in search applications increases. On the other hand companies struggle to actually evaluate the impact of vector search and compare the relevance to traditional techniques like BM25.
This talk will briefly introduce business cases for applying vectors in search before technically demonstrating an open source reference implementation of vector search that incorporates different ways of leveraging vectors in search.
Finally, I want to show how to evaluate relevance gains (or losses) when using vector search, to have actual proof of how useful the vector based approaches are.
After attending this talk you will know how to get started with vector search, what different approaches there are to integrate vectors in search applications and how to measure their impact on search relevance.

Daniel has worked in search since graduating in computational linguistics studies at Ludwig-Maximilians-University Munich in 2012 where he developed his weakness for search and natural language processing. His experience as a search consultant paved the way for becoming an O’Reilly author co-authoring the first German book on Apache Solr.
He enjoys combining multiple open source tools like Apache Zeppelin or Apache NiFi to build powerful search stacks to deliver relevant search.
In his free time he supports the local fire brigade as a volunteer firefighter and serves as the sports director of the local shooting club in the village he lives in.

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