allentv / wwc-recsys

a recommender systems interactive session, by Humberto Corona (@totopampin)

This is the repo for a recommender systems interactive session (or hacknight). Here you will find all the relevant resources, as well as the code that will be used.

The goal of the hacknight is to introduce people to recommender sytems, build your own recommender system and evaluate it by generating recommendations for you and other attendees.

The code supports a hacknight, which should be a relaxed and interactive environments, with people helping you to go through the examples. If you want, you can join a friend and code together.

Hacknight

• 6:00 - 6:30 networking and food 🍕
• 6:30 - 6:50 presentation (introduction to Recommender Systems) 📊
• 6:50 - 8:20 hacking! 💥 🎉
• 8:20 - 8:45 wrap up, questions, show and tell 👏

Before the session:

Before the session, please read and follow all the steps detailed in the instructions file. If you have problems following it, you can ask questions in the meetup event forum, or using github issues.

Examples of things you can do or questions that are interesting?

• It looks like some of the recommendations are from items we just rated. However, you want people to discover new movies. How do you make sure you never get recommendations for items you have rated?

• Are scores important? Should we filter the recommendations to only show the ones we are really sure people will like?

• What do we know about the dataset? Start an ipython notebook and plot some basic statistics, it will help you understand many things about. For example, how many ratings does each movie gets? What are the most popular movies? what is the rating distribution?

• See who are your neighbours, see what they have rated. This is a non-automated way to discover movies, and it also helps to understand how the U-KNN algorithm works.

• Play with the parameters. For example, are your neighbours better when you use 'pearson correlation' or when you use 'cosine similarity'?

• Make recommendations for one of your colleagues. Do they like them? Is their perception on quality the same as yours?

• How long will it take for each algorithm to generate recommendations for all users? Is it scalable? Which one is better?

• Are the recommendations good? Which algorithm makes better recommendations?

• Implement the matrix factorization algorithm. It works very well, and it is very similar to the ones used in real-world recommender systems!

Resources

• 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
• Collaborative Filtering on Wikipedia
• An introduction to RS from Humberto's talk at #unit conference: slides

Additional resources

• Recommendations with Apache Spark
• The Movielens dataset website
• ACM Recommender Systems Wiki