Description
1.1 Chapter 9
Problems: 9.2.1,9.2.3,9.3.1,9.4.1
2 Practicum Problems
These problems will primarily reference the lecture materials and the examples
given in class using Python. It is suggested that a Jupyter/IPython notebook
be used for the programmatic components.
2.1 Problem 1
Load the Movielens 100k dataset (ml-100k.zip) into Python using Pandas
dataframes. Build a user profile on centered data (by user rating) for both users
200 and 15, and calculate the cosine similarity and distance between the user’s
preferences and the item/movie 95. Which user would a recommender system
suggest this movie to?
2.2 Problem 2
Load the Movielens 100k dataset (ml-100k.zip) into Python using Pandas
dataframes. Convert the ratings data into a utility matrix representation, and
find the 10 most similar users for user 1 based on cosine similarity of the centered
user ratings data. Based on the average of of the ratings for item 508 from the
similar users, what is the expected rating for this item for user 1?
Prof. Panchal:
Thu. 6:45PM-9:35PM
CS 422 Data Mining
