Meetup News
london information retrieval meetup
london information retrieval meetup

After the very warm reception of the first and second edition, the third London Information Retrieval Meetup is approaching (21/10/2019) and we are excited to add more details about our speakers and talks!

// LONDON INFORMATION RETRIEVAL MEETUP

PROGRAMME

After a short welcome & latest news speech from our Founder Alessandro Benedetti, we will proceed to the first talk.

// first talk

How to Build your Training Set for a Learning to Rank Project

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), organizations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to : 
– model and collect the necessary feedback from the users (implicit or explicit)
– calculate for each training sample a relevance label that is meaningful and not ambiguous (Click Through Rate, Sales Rate …)
– transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training libraries expect)
Join us as we explore real-world scenarios and dos and don’ts from the e-commerce industry.

// speaker

Alessandro Benedetti

Founder @ Sease

APACHE SOLR PMC MEMBER
APACHE LUCENE/SOLR COMMITTER

Alessandro has been involved in designing and developing search-relevant solutions from the early stages of Apache Solr 1.4 and edismax query parser in 2010. Over the years he has worked on various projects aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.

// second talk

Music Information Retrieval Take 2: Interval Hashing Based Ranking

Retrieving musical records from a corpus of Information, using an audio input as a query is not an easy task. Various approaches try to solve the problem modelling the query and the corpus of Information as an array of hashes calculated from the chroma features of the audio input.
Scope of this talk is to introduce a novel approach in calculating such hashes, considering the intervals of the most intense pitches of sequential chroma vectors.
Building on the theoretical introduction, a prototype will show you this approach in action with Apache Solr with a sample dataset and the benefits of positional queries.
Challenges and future works will follow up as conclusive considerations.

// speaker

Andrea Gazzarini

Co-Founder @ Sease

RRE CREATOR

Andrea Gazzarini is a curious software engineer, mainly focused on the Java language and Search technologies. With more than 15 years of experience in various software engineering areas, his adventure in the search world began in 2010, when he met Apache Solr and later Elasticsearch.

// london information retrieval meetup

Join our Group

Researchers, scientists, and other practitioners in the field of Information Retrieval, Machine Learning, and Data Science… join us, and let’s create a group of passionate and professionals!

Author

Alessandro Benedetti

Alessandro Benedetti is the founder of Sease Ltd. Senior Search Software Engineer, his focus is on R&D in information retrieval, information extraction, natural language processing, and machine learning.

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