More on “More Like This” Recommendations in SOLR
As a Software Engineer and Technical Team Lead, Oana Brezai designed and implemented solutions for clients in various industries such as: retail, banking, insurance, automotive, public administration and telecom.
She is passionate about Information Retrieval and works mostly with open source tools, SOLR being one of them. In her free time, she is actively involved in a public speaking club.
Areas of expertise:
Building software & products, involved in architecture, design, development and release of software and solutions. Implementing complete software development life-cycle using Agile principles.
Cloud Architecture – Leveraging cloud technology for obtaining flexible and scalable solutions.
Integrating and implementing digital transformation initiatives & efforts to streamline and modernize an organization’s processes.
Companies like Amazon, Netflix, and Youtube have popularized mantras like “More Like This” recommendations. Now, almost all online shops/content sites implement such solutions.
But is it possible to build a scalable Content-Based Recommendation System using open-source software that is easy-to-maintain, simple to tune and straightforward to deploy?
I will present how to use “More Like This” from Apache SOLR. Built as a Search Tool, Apache SOLR can also be used as a Recommendation System as they both operate with computing relevance.
However, the “More Like This” functionality of SOLR uses only text fields. I will show you how to overcome this and fully profit from the powerful capabilities of SOLR.
I will also present how an Inverted Index works, the TF/IDF scoring formula and how to measure the performance of a Recommendation System. All with a step-by-step example.