TOA Satellite Event: Build your own Recommender System!

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**PLEASE SIGN UP FOR THIS EVENT ON EVENTBRITE USING THIS LINK** (https://www.eventbrite.com/e/build-your-own-recommender-system-tickets-35781677962)

No, we’re not reading your mind.

Ever notice how so much of your online experience is curated to you - recommended outfits on Zalando, suggested songs or books. In this interactive workshop, we will introduce and practice the most important concepts about recommender systems, the force behind the personalized experience.

By the end of the workshop, you will be able to build your own recommender system and discuss the outcomes with the help of our mentors, all in a very informal and interactive way. If you’ve got basic Python knowledge, you’re a great fit for this workshop. Bring along a friend and code together!

Schedule:

12.30 - 1.15 pm: Lunch

1.15 - 1.45 pm: 101 Recommender Systems

1.45 - 3.15 pm: Hacking Recommender Systems!

3.15 - 3.45 pm: Wrap Up discussion

Before the Session:

We advise that you visit the workshop website (http://hcorona.github.io/recsys-101-workshop/) and read the instruction file (http://hcorona.github.io/recsys-101-workshop/docs/instructions.html)at least a day before the workshop, so you have everything you need to start coding already installed. If you have problems following it, you can ask questions by submitting a GitHub issue and we will answer them. It is really important to have Python3 installed in your laptop.

Some additional resources to read:

Collective Intelligence book : Programming Collective Intelligence book (http://www.amazon.com/gp/product/0596529325/ref=as_li_qf_sp_asin_il?ie=UTF8&camp=1789&creative=9325&creativeASIN=0596529325&linkCode=as2&tag=tasktoy-20)

Collective Intelligence (book code): Programming Collective Intelligence code (https://github.com/cataska/programming-collective-intelligence-code)

Collaborative Filtering on Wikipedia (https://en.wikipedia.org/wiki/Collaborative_filtering)

Additional resources with general information:

Recommendations with Apache Spark (https://www.codementor.io/spark/tutorial/building-a-recommender-with-apache-spark-python-example-app-part1)

The Movielens dataset website (http://grouplens.org/datasets/movielens/)

ACM Recommender Systems Wiki (http://www.recsyswiki.com/wiki/)

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Important Note: Please remember to bring your ID and Eventbrite confirmation for admission.

Attendees (3)