Description
In this assignment you will build recommender systems to make predictions related to book reviews from
Goodreads.
Solutions will be graded on Kaggle (see below), with the competition closing at 5pm, Monday November
19 (note that the time reported on the competition webpage is in UTC!).
You will also be graded on a brief report, to be submitted electronically on gradescope by the following
day. Your grades will be determined by your performance on the predictive tasks as well as your written report
about the approaches you took.
This assignment should be completed individually. To begin, download the files for this assignment from:
http://cseweb.ucsd.edu/classes/fa19/cse258-a/files/assignment1.tar.gz
Files
train Interactions.csv.gz 200,000 ratings to be used for training. This data should be used for the ‘read
prediction’ (both classes) and ‘rating prediction’ (CSE258 only) tasks. It is not necessary to use all
ratings for training, for example if doing so proves too computationally intensive.
userID The ID of the user. This is a hashed user identifier from Goodreads.
bookID The ID of the book. This is a hashed book identifier from Goodreads.
rating The star rating of the user’s review.
train Category.json.gz Training data for the category prediction task (CSE158 only). This file is json
formatted, and contains the following fields:
n votes The number of ‘likes’ this review has received.
review id A hashed identifier for this review.
user id A hashed identifier for the user.
review text Text of the review.
rating Rating of the book.
genreID A numeric label associated with the genre.
genre A string version of the genre.
test Category.json.gz Test data associated with the category prediction task. This data has the same format
as above, with the ‘genre’ and ‘genreID’ labels hidden.
pairs Read.txt Pairs on which you are to predict whether a book was read (both classes).
pairs Category.txt Pairs (userID and reviewID) on which you are to predict the category of a book (CSE158
only).
pairs Rating.txt Pairs (userIDs and bookIDs) on which you are to predict ratings (CSE258 only).
baselines.py A simple baseline for each task, described below.
Please do not try to collect these reviews from Goodreads, or to reverse-engineer the hashing function I
used to anonymize the data. Doing so will not be easier than successfully completing the assignment. We will
request working code for any solution suspected of violating the competition rules.
Tasks
You are expected to complete the following tasks:
Read prediction (both classes) Predict given a (user,book) pair from ‘pairs Read.txt’ whether the user
would read the book (0 or 1). Accuracy will be measured in terms of the categorization accuracy (fraction
of correct predictions). The test set has been constructed such that exactly 50% of the pairs correspond
to read books and the other 50% do not.
Category prediction (CSE158 only) Predict the category of a book from a review. Five categories are
used for this task, which can be seen in the baseline program, namely Children’s, Comics/Graphic Novels,
Fantasy, Mystery/Thriller, and Young Adult. Performance will be measured in terms of the fraction of
correct classifications.
Rating prediction (CSE258 only) Predict people’s star ratings as accurately as possible, for those (user,item)
pairs in ‘pairs Rating.txt’. Accuracy will be measured in terms of the mean-squared error (MSE).
A competition page has been set up on Kaggle to keep track of your results compared to those of other
members of the class. The leaderboard will show your results on half of the test data, but your ultimate score
will depend on your predictions across the whole dataset.
Grading and Evaluation
This assignment is worth 25% of your grade. You will be graded on the following aspects. Each of the two
tasks is worth 10 marks (i.e., 10% of your grade), plus 5 marks for the written report.
• Your ability to obtain a solution which outperforms the leaderboard baselines on the unseen portion of
the test data (5 marks for each task). Obtaining full marks requires a solution which is substantially
better than baseline performance.
• Your ranking for each of the tasks compared to other students in the class (3 marks for each task).
• Obtain a solution which outperforms the baselines on the seen portion of the test data (i.e., the leaderboard). This is a consolation prize in case you overfit to the leaderboard. (2 mark for each task).
Finally, your written report should describe the approaches you took to each of the tasks. To obtain good
performance, you should not need to invent new approaches (though you are more than welcome to!) but
rather you will be graded based on your decision to apply reasonable approaches to each of the given tasks (5
marks total).
Baselines
Simple baselines have been provided for each of the tasks. These are included in ‘baselines.py’ among the files
above. They are mostly intended to demonstrate how the data is processed and prepared for submission to
Kaggle. These baselines operate as follows:
Read prediction Find the most popular books that account for 50% of interactions in the training data.
Return ‘1’ whenever such a book is seen at test time, ‘0’ otherwise.
Category prediction Look for a few likely words that may appear in reviews of each category (e.g. if the
word ‘fantasy’ appears, classify as Fantasy).
Rating prediction Return the global average rating, or the user’s average if we have seen them before in the
training data.
Running ‘baselines.py’ produces files containing predicted outputs (these outputs can be uploaded to Kaggle). Your submission files should have the same format.
Kaggle
We have set up a Kaggle page to help you evaluate your solution. You should be able to access the competition
via public links. The Kaggle pages for each of the tasks are:
https://www.kaggle.com/c/cse158258-fa19-read-prediction
https://www.kaggle.com/c/cse158-fa19-category-prediction
https://www.kaggle.com/c/cse258-fa19-rating-prediction
You are welcome to attempt the tasks from either class, but will only be graded on the tasks from your
own class.
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