BYU CS classes

To understand recommender systems, and to have a significant, creative experience exploring a large dataset in a competition-style setting.

For this lab, you will construct a movie recommendation engine, using a simple publicly available dataset. For this lab, you will turn in three things:

1. A notebook containing your code, but we will not run it.
2. A set of predictions for a specific list of <user,movie> pairs.
3. A report discussing your approach, how well it worked (in terms of RMSE), and any visualizations or patterns you found in the data.

We will run a small “competition” on your predictions: the three students with the best predictions will get 10% extra credit on this lab.

You may use any strategy you want to construct your predictions, except for attempting to determine the values of the missing entries by analyzing the original dataset.

Your entry will be graded on the following elements:

• 100% Project writeup
• 10% extra credit for the three top predictions

This lab is designed to help you be creative in finding your own way to solve a significant data analysis problem. You may use any of the techniques we have discussed in class, techniques from other classes, or you may invent your own new techniques.

The training set you will use can be downloaded here:

Movie ratings training data

A complete description of the data can be found in the readme.txt file. This dataset is richer than the Netflix competition dataset; for each movie, you also have a corresponding IMDB ID, some RottenTomatoes information, as well as a set of tags that users may have used when rating each movie.

You should start by looking at the user_ratedmovies_train.dat file. It is a CSV file containing user,movie,timestamp tuples that form the core training data. Everything else is auxiliary data that may or may not be useful.

Turning in your submissions

As part of this lab, you must submit a set of predictions. You must provide predictions as a simple CSV file with three columns and 85,000 rows. Each row has the form

test_id,predicted rating

The test_id field

Evaluating your submissions

Performance of your prediction engine will be based on RMSE:

$$ \mathrm{RMSE} = \sqrt{ \sum_{i} (\mathrm{prediction_i} - \mathrm{truth_i})^2 } $$

Note: it is strongly encouraged that you first partition your dataset into a training and a validation set, to assess the generalization performance of your rating algorithm!

import matplotlib.pyplot as plt
import seaborn
import pandas
 
ur = pandas.read_csv('user_ratedmovies_train.dat','\t')
 
plt.hist( ur['rating'] )