The Fifth Elephant 2015

Will talk about classical and state-of-the-art recommender systems. The audience will also get a flavour of the mathematical computations that go into recommender systems.

Outline

Recommender Systems solves matrix sparsity problem. And this idea of predicting sparse values can be applied for various problems across domains. I have used recommender systems to identify audience clusters for a conference, recommending new jokes to users based on the past jokes they liked, and few kaggle problems.

One of the key events that energized research in recommender systems was the Netflix prize. Netflix sponsored a competition, that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company’s existing recommender system.

Recommender systems typically produce a list of recommendations in one of the two ways - through collaborative or content-based filtering. Would like to cover both of them with the implementation and mathematics involved.

Requirements

Familiarity with Matrices and basic algebra

Speaker bio

Currently part of data science team at Fidelity Investments, Business Analytics and Research.

Master’s in Mathematics from BITS, Pilani.
Am an open-source enthusiast and Kaggler.

https://in.linkedin.com/in/kswaroop

Links

• https://atlas.mindmup.com/2015/06/f4f24c00fc1201325f030aa95dd006d1/recommender_systems_rs_/index.html