Objective
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.
Description
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 two ways - through collaborative or content-based filtering. Would like to cover both of them with the implementation and mathematics involved.
Speaker bio
Currently working as a Senior Analyst at TCS R&D. Over 4 years of experience in analytics, passionate about Data Mining, Modelling & number driven predictive analytics.
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Good Work dude..keep it up..
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Hi
Please share what libraries will you be using to explain what goes under the hood in reco. systems.
Also, what all datasets and insights from it can the audiences expect.
Would be great if you can list / share the reco. systems that you have built.
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I have written code in Python for this. I have not used any libraries. The recommendation systems problem is generic and can be applied to data in any domain, I have personally worked on movie recommendations, retail datasets etc.
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Hi Swaroop,
Can you share the code snippet. ?
Also which reco. algorithm have you implemented.