After the very warm reception of the first year, the fifth London Information Retrieval Meetup is approaching (23/06/2020) and we are excited to add more details about our speakers and talks!
The event is going to be fully remote (given the COVID-19 situation) and free!
After a short welcome & latest news speech from our Founder Alessandro Benedetti, we will proceed to the first talk.
first talk
Evaluating Your Learning to Rank Model: Dos and Don’ts in Offline/Online Evaluation
Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular (Apache Solr supports it from Jan 2017 and Elasticsearch has an Open Source plugin released in 2018), organizations struggle with the problem of how to evaluate the quality of the models they train.
This talk explores all the major points in both Offline and Online evaluation.
Setting up correct infrastructures and processes for a fair and effective evaluation of the trained models is vital for measuring the improvements/regressions of a LTR system.
The talk is intended for:
– Product Owners, Search Managers, Business Owners
– Software Engineers, Data Scientists, and Machine Learning Enthusiast
Expect to learn :
- the importance of Offline testing from a business perspective
- how Offline testing can be done with Open Source libraries
- how to build a realistic test set from the original data set in input avoiding common mistakes in the process
- the importance of Online testing from a business perspective
- A/B testing and Interleaving approaches: details and Pros/ Cons
- common mistakes and how they can false the obtained results
Join us as we explore real world scenarios and dos and don’ts from the e-commerce industry!
the speakers
Alessandro Benedetti
FOUNDER @ SEASE
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Senior Search Software Engineer, his focus is on R&D in Information Retrieval, Information Extraction, Natural Language Processing, and Machine Learning.
He firmly believes in Open Source as a way to build a bridge between Academia and Industry and facilitate the progress of applied research.
Anna Ruggero
R&D SOFTWARE ENGINEER @ SEASE
Anna Ruggero is a software engineer passionate about Information Retrieval and Data Mining.
She loves to find new solutions to problems, suggesting and testing new ideas, especially those that concern the integration of machine learning techniques into information retrieval systems.
second talk
Enterprise Search - How Relevant Is Relevance?
Enterprise search is the outlier in search applications. It has to work effectively with very large collections of un-curated content, often in multiple languages, to meet the requirements of employees who need to make business-critical decisions.
In this talk, I will outline the challenges of searching enterprise content. Recent research is revealing a unique pattern of search behavior in which relevance is both very important and yet also irrelevant, and where recall is just as important as precision. This behavior has implications for the use of standard metrics for search performance (especially in the case of federated search across multiple applications) and for the adoption of AI/ML techniques.
the speaker
Martin White
Martin White is an information scientist who has been working with IR systems since 1974. Over the last twenty years at Intranet Focus he has worked on nearly 100 search-based projects, mainly in the pharmaceutical, engineering, legal and NGO sectors. He is the author of four books on enterprise search and has given presentations and workshops in Europe and North America.
