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
