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Lessons from the Membership Puzzle Project Summit'21

Lessons from the Membership Puzzle Project Summit'21

The Membership Puzzle project that ran for 4 successful years comes to an end in a few days. Here are some lessons from their recent MPP Summit. Have a read.

What is the cookie apocalypse and why should you care?

machine learning

What is the cookie apocalypse and why should you care?

In this article, we look at ways for publishers to live in the privacy conscious world.

Part II - Explainable ML Models: Local post hoc explanations

Part II - Explainable ML Models: Local post hoc explanations

Post hoc explanations approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Several local explanation methods are model-agnostic, meaning they do not have access to the internal structure of the model.

Explainable ML Models: what are explanations and why do we need them? – Part I

Explainable ML Models: what are explanations and why do we need them? – Part I

Interpretability is a key element of trust for AI models. An explanation is an interpretable description of a model behavior. For an explanation to be valid it needs to be faithful to the model and it needs to be understandable to the user.

Personalization, Privacy, and Me

Personalization

Personalization, Privacy, and Me

TL;DR At recsyslabs we are building AI powered products that provide personalized recommendations to readers without harvesting personal data. Online content personalization is not a solved problem. People are growing concerned of their data being collected in excess and the usage of it for purposes other than the ones