Modeling and Counteracting Exposure Bias in Recommender Systems

What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine-learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after…

Read More

An end-to-end intelligent assistance tool for crowdfunding projects

Estimate the probability of success of crowdfunding projects using machine learning classification algorithms and develop a Ruby library to help users boost their campaigns. This work has been done during my Data Science Internship at DigiSponsor in Paris Collect (scrape) crowdfunding project’s data from Kickstarter using python Perform exploratory statistics and analysis on the data…

Read More

Explainable Active Learning Strategy For Recommender Systems

Recommender System technologies are witnessing a revolutionary era these days. They are growing more and more important since they help in the discovery and promotion of products, books, news, music, movies, courses, restaurants, etc. One major problem of Recommender Systems is that the most accurate models tend to be black boxes, whose results cannot be…

Read More