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Mini Project 2: Recommender Systems
CIS 415 Big Data in Business
Due date: April 23, 2020, 11:59pm

Problem Definition
Let’s say we have 8 users, and they have rated 8 different albums on a scale of 1 to 5. Note that
not all users have rated all albums.
songData3 = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5,
"Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},
"Bill":{"Blues Traveler": 2.0, Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly
Stoopid": 3.5, "Vampire Weekend": 3.0}, "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0,
"Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0}, "Dan": {"Blues
Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5,
"The Strokes": 4.0, "Vampire Weekend": 2.0}, "Hailey": {"Broken Bells": 4.0, "Deadmau5":
1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0}, "Jordyn": {"Broken
Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The
Strokes": 4.0, "Vampire Weekend": 4.0}, "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0,
"Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0}, "Veronica":
{"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The
Strokes": 3.0}}
Requirement for this Assignment
You have been provided with a framework for this assignment: “MiniProject-2-Framework.py”.
The Framework defines a Class called similarity. You must use the class as-is with no changes.
The class includes a class initialization method that takes two rating dictionaries ratingP and
ratingQ as parameters. The class has two methods:
 minkowski which takes a single parameter r, and returns the Minkowski Distance
between the two dictionaries (that the Class object is instantiated with).
 pearson which takes no parameters, and returns the Pearson Correlation between the two
dictionaries (that the Class object is instantiated with).
Given a userX, write code to find the NN (k=1) recommendations for userX based on the
Euclidean Similarity measure. A pseudo-code has been provided to you in the framework. So,
you essentially need to plug in appropriate code for steps 1 through 5 in the framework.
Since this is the same data as used in the Recommendation Systems Lecture, you should check
your output against the lecture for accuracy.
Submission
File1: Single Python script
Some things to keep in mind as you code:
 Make your code readable – for instance, use meaningful variable names and comments.
 Make your code elegant – for instance, balance the number of variables you introduce –
too many or too few make your code difficult to debug, read, and maintain.
 Make your output readable and user-friendly
Once you have written up the script, save it as follows. Submit the script by uploading your
python script. Note: upload the actual script – DO NOT attach a screenshot of the script!
CIS415RecommendationSystemMP_.py
[Example: CIS415RecommendationSystemMP_AditiDatta.py]

The submitted script will be run as-is for grading using the Anaconda Spyder 3.7.0 installation
required for this class. Points will be deducted for scripts that:
 are difficult to read/follow
 don’t compile/run
 don’t have all the various pieces of code required
 have hard-code values instead of using variables
 have logical errors
 don’t result in the expected output
 don’t have user-friendly output
 don’t’ run efficiently following the practices and lecture examples demonstrated in our
lectures and problem sets (located in the course site)
File2: In a single PDF document. Briefly state the approach of building the recommender system
model and explain the code and the workflow of building this project in PDF document (1.0
points). Use the following naming convention for your final document:
CIS415RecommendationSystemMP_.pdf
[Example: CIS415RecommendationSystemMP_AditiDatta.pdf]

Supporting Files:
MiniProject-2-Framework.py

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