Question description
Item-Based Joke Recommendation [Dataset: jokes.zip]For this problem you will use a modified version of the item-based recommender algorithm from Ch. 14 of Machine Learning in Action and use it on joke ratings data based on Jester Online Joke Recommender System. The modified version of the code is provided in the module itemBasedRec.py. Most of the module will be used as is, but you will add some additional functionality.The data set contains two files. The file “modified_jester_data.csv” contains the ratings on 100 jokes by 1000 users (each row is a user profile). The ratings have been normalized to be between 1 and 21 (a 20-point scale), with 1 being the lowest rating. A zero indicated a missing rating. The file “jokes.csv” contains the joke ids mapped to the actual text of the jokes.Your tasks in this problem are the following (please also see comments for the function stubs in the provided module):Load in the joke ratings data and the joke text data into appropriate data structures.Complete the definition for the function “test”. This function iterates over all users and for each performs cross-validation on items (by calling the provided “cross_validate_user” function), and returns the error information necessary to compute Mean Absolute Error (MAE). Use this function to perform 5-fold cross-validation (i.e., 20% test-ratio) comparing MAE results using standard item-based collaborative filtering (based on the rating prediction function “standEst”) with results using the SVD-based version of the rating item-based CF (using “svdEst” as the prediction engine). [Note: See comments provided in the module for hints on accomplishing these tasks.]Write a new function “print_most_similar_jokes” which takes the joke ratings data, a query joke id, a parameter k for the number of nearest neighbors, and a similarity metric function, and prints the text of the query joke as well as the texts of the top k most similar jokes based on user ratings. [Note: For hints on how to accomplish this task, please see comments at the end of the provided module as well as comments for the provided stub function.]
