Joke Rating Prediction

Many online businesses rely on customer reviews and ratings. Explicit feedback is especially important in the entertainment and ecommerce industry where all customer engagements are impacted by these ratings. Netflix relies on such rating data to power its recommendation engine to provide best movie and TV series recommendations that are personalized and most relevant to the user.

This practice problem challenges the participants to predict the ratings for jokes given by the users provided the ratings provided by the same users for another set of jokes. This dataset is taken from the famous jester online Joke Recommender system dataset.

We thank Dr. Ken Goldberg's group for putting this super cool data together and for permission to share it with the AV community.

Reference:

Eigentaste: A Constant Time Collaborative Filtering Algorithm. Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Information Retrieval, 4(2), 133-151. July 2001.

Data Science Resources

• Are you a beginner? If yes, you can check out our latest'Intro to Data Science' course to kickstart your journey in data science.
• You can refer to this article to learn more about rating predictions

Rules

• One person cannot participate with more than one user accounts.
• You are free to use any tool and machine you have rightful access to.
• You can use any programming language or statistical software.
• You are free to use solution checker as many times as you want.