Microsoft Word – Assignment 4.docx
INF 553 – Spring 2017
Assignment 4: LSH For Similar Movies
Deadline: 04/03/2017 11:59 PM PST
Assignment Overview
In this assignment you are asked to implement the LSH technique using the Spark Framework in order to
identify similar movies based on the ratings of the users. You can use any Spark version that is greater
or equal to 1.6 but you need to specify the version in your description file. You will use only one
dataset in order to find the similar movies as described in the following sections. The goal of this
assignment is to help you understand how you can use the LSH algorithm to identify similar items in an
efficient way by programming it within a distributed environment.
Write your own code!
For this assignment to be an effective learning experience, you must write your own code!
Do not share code with other students in the class!!
Here’s why:
● The most obvious reason is that it will be a huge temptation to cheat: if you include code written
by anyone else in your solution to the assignment, you will be cheating. As mentioned in the
syllabus, this is a very serious offense, and may lead to you failing the class.
● However, even if you do not directly include any code you look at in your solution, it surely will
influence your coding. Put another way, it will short-circuit the process of you figuring out how
to solve the problem, and will thus decrease how much you learn.
So, just don’t look on the web for any code relevant to these problems. Don’t do it.
Submission Details
For this assignment you will need to turn in a Python, Java, or Scala program depending on your language
of preference. We will test your code using the same dataset in order to verify its correctness. This
assignment will surely need some time to be implemented so please plan accordingly and start early!
Your submission must be a .zip file with name:
your submission should be identical as shown below. The Firstname_Lastname_Description.pdf file
contains helpful instructions on how to run your code along with the answers to the questions of the
following sections. The OutputFiles directory contains the required deliverable output files and the
Solution directory contains your source code.
LSH Algorithm
In this assignment you will need to develop the LSH technique using the ml-latest-
small/ratings.csv file from the MovieLens dataset. The dataset is provided to you under the /data
folder of the bundled .zip file of the assignment. The goal of this assignment is to find similar
movies according to the ratings of the users. In order to solve this problem, you will need to read
carefully the sections 3.3 – 3.5 from Chapter 3 of the Mining of Massive Datasets book.
In this problem, we will focus on 0-1 ratings rather than the actual ratings of the users. To be
more specific, if a user has rated a movie, then his contribution to the characteristic matrix is 1
while if he hasn’t rated a movie his contribution is 0. Our goal is to identify similar movies
whose Jaccard Similarity is greater or equal to 0.5.
Implementation Guidelines – Approach
The original characteristic matrix must be of size [users] x [movies]. Each cell contains a 0 or 1
value depending on whether the user has rated the movie or not. Once the matrix is built you are
free to use any collection of hash functions that you think would result in a more consistent
permutation of the row entries of the characteristic matrix.
Some potential hash functions could be of type:
f(x)= (ax + b) % m
or
f(x) = ((ax + b) % p) % m
where p is any prime number and m is the number of bins.
You can use any value for the a, b, p or m parameters of your implementation.
Once you have computed all the required hash values you must build the Signature Matrix. Once
the Signature Matrix is built you must divide the Matrix into b bands with r rows each, where
bands x rows = n (n is the number of hash functions), in order to generate the candidate pairs.
Remember that in order two movies to be a candidate pair their signature must agree (i.e. be
identical) with at least one band.
Once you have computed the candidate pairs, your final result will be the candidate pairs whose
Jaccard Similarity is greater or equal to 0.5. After computing the final similar items, you must
compare your results against the provided ground truth dataset using the precision and recall
metrics. The ground truth dataset contains all the movies pairs that have Jaccard similarity above
or equal to 0.5. The ground truth dataset is located under the /data folder of the assignment’s
bundled .zip file, and named as SimilarMovies.Goundtruth.05.csv.
Example of Jaccard Similarity:
user1 user2 user3 user4
movie1 0 1 1 1
movie2 0 1 0 0
Jaccard Similarity (movie1, movie2) = #movies_intersection / #movies_union = 1/3
Execution Example
The program that you will implement should take two parameters as input and generate one file
as an output. The first parameter must be the location of the ratings.csv file and the second one
must be the path to the output file followed by the name of the output file. The name of the
output file must be firstname_lastname_SimilarMovies.txt. The content of the file must follow
the exact same directions of question 1 in the Questions & Grades Breakdown section below. If
your program does not generate this file or it does not follow the specifications as described in
the following section, there would be a penalty of 50%.
Java/Scala Execution Example:
Python Execution Example:
Finally, inside the description file of your submission write how to run your code, which Spark
version you used and if you use Java or Scala, specify the language versions as well.
Questions & Grade Breakdown
1. A file that contains the pairs of similar movies that your algorithm has computed
using LSH followed by their Jaccard similarity.
(40 Points)
Example Format:
movie1, movie2, Jaccard-Similarity12
movie1, movie3, Jaccard-Similarity13
…
movien, moviek, Jaccard-Similaritynk
The file must be sorted first by the left-hand side movie and then by the right-hand side
movie. For example, the first row on the above snippet should be shorted firstly by
movie1 and secondly by movie2.
The file should be located under the OutputFiles directory of your submission
with the name:
firstname_lastname_SimilarMovies.txt
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2. For Jaccard Similarity >= 0.5 compute the precision and recall for the similar
movies that your algorithm has generated against the ground truth data file under
the data folder in the .zip file.
(40 Points)
Expressions:
Precision = tp / (tp + fp)
Recall = tp / (tp + fn)
tp: true positives
fp: false positives
fn: false negatives
In order to get full credit for this question you should have precision >= 0.9 and
recall >= 0.75. If not, then you will get partial credit based on the formula:
(Precision/0.9) * 20 + (Recall/0.75) * 20
3. In your description file you must include a table similar to the following table from
Section 3.4.2 of Chapter 3 from the Mining Massive Datasets book (Figure 3.8).
(15 Points)
The table should include the values of the S-Curve for the number of bands (b) and rows
(r) you have divided your signature Matrix.
For instance, the following table is computed using b=20 and r=5
s 1-(1-sr)b
0.2 0.006
0.3 0.047
0.4 0.186
0.5 0.470
0.6 0.802
0.7 0.975
0.8 0.9996
4. In your description file, for the table you have computed above, answer to the
following question.
(5 Points)
How does the value of b and r affect the value of the threshold for the movies dataset?
General Instructions:
1. Make sure your code compiles before submitting
2. Make sure to follow the output format and the naming format.
Grading Criteria:
1.If your programs cannot be run with the commands you provide, your submission will be graded
based on the result files you submit and 20% penalty for it.
2. If the input parameters are more than the ones requested there will be 50% penalty.
3. If the output file does not follow the specifications requested there will be 50% penalty.
4.If your program generates more than one file, there will be 20% penalty.
5. If you don’t provide the source code and just the .jar file in case of a Java/Scala application
there will be 60% penalty.
6. If your submission does not state inside the Description pdf file how to run your code and
which Spark version you used there will be a penalty of 20%.
7.There will be 20% penalty for late submission.
Appendix A
A.1 There is no limit on the number of hash functions you can use.
A.2 You could resemble the number of bands with the number of partitions of your RDD.
A.3 If you want to improve your algorithm’s efficiency you could use the Vectors from
Spark MLlib to perform your operations.
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