Movie Recommendations via Item-Item Collaborative Filtering. You are provided with real data (Movie-Lens dataset) of user ratings for different movies. There is a readme file that describes the data format. In this project, you will implement the item-item collaborative filtering algorithm that we discussed in the class. The high-level steps are as follows: a) Construct the profile of each item (i.e., movie). At the minimum, you should use the ratings given by each user for a given item (i.e., movie). Optionally, you can use other information (e.g., genre information for each movie and tag information given by the user for each movie) creatively. If you use this additional information, you should explain your methodology in the submitted report. b) Compute similarity score for all item-item (i.e., movie-movie) pairs. You will employ the centered cosine similarity metric that we discussed in class. c) Compute the neighborhood set N for each item (i.e. movie). You will select the movies that have the highest similarity score for the given movie. Please employ a neighborhood of size 5. Break ties using lexicographic ordering over movie-ids. d) Estimate the ratings of other users who didn’t rate this item (i.e., movie) using the neighborhood set. Repeat for each item (i.e., movie). e) Compute the recommended items (movies) for each user. Pick the top-5 movies with the highest estimated ratings. Break ties using lexicographic ordering over movie-ids. Your program should output top-5 recommendations for each user.

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

Please write Python code.

Movie Recommendations via Item-Item Collaborative Filtering. You are provided with real data (Movie-Lens dataset) of user ratings for different movies. There is a readme file that describes the data format. In this project, you will implement the item-item collaborative filtering algorithm that we discussed in the class. The high-level steps are as follows:

a) Construct the profile of each item (i.e., movie). At the minimum, you should use the ratings given by each user for a given item (i.e., movie). Optionally, you can use other information (e.g., genre information for each movie and tag information given by the user for each movie) creatively. If you use this additional information, you should explain your methodology in the submitted report.

b) Compute similarity score for all item-item (i.e., movie-movie) pairs. You will employ the centered cosine similarity metric that we discussed in class.

c) Compute the neighborhood set N for each item (i.e. movie). You will select the movies that have the highest similarity score for the given movie. Please employ a neighborhood of size 5. Break ties using lexicographic ordering over movie-ids.

d) Estimate the ratings of other users who didn’t rate this item (i.e., movie) using the neighborhood set. Repeat for each item (i.e., movie).

e) Compute the recommended items (movies) for each user. Pick the top-5 movies with the highest estimated ratings. Break ties using lexicographic ordering over movie-ids. Your program should output top-5 recommendations for each user.

	movieId_1	movieId_2	movieId_3	movieId_4	movieId_5	movieId_6	movieId_7	movieId_8	movieId_9	movieId_10
movieId_1	1	0.1431	0.07688	0.008127	0.09366	0.01457	0.1098	0.005078	-0.05931	0.02976
movieId_2	0.1431	1	0.02305	0.04271	0.009544	-0.00369	0.1035	0.1233	0.05253	0.193
movieId_3	0.07688	0.02305	1	0.01552	0.2279	-0.00042	0.02262	0.1398	0.1029	0.02342
movieId_4	0.008127	0.04271	0.01552	1	0.09151	-0.02098	0.1853	0.1497	-0.07277	0.03346
movieId_5	0.09366	0.009544	0.2279	0.09151	1	0.1149	0.005068	0.1279	0.08839	0.01301
movieId_6	0.01457	-0.00369	-0.00042	-0.02098	0.1149	1	-0.00661	0.005829	0.04757	-0.01539
movieId_7	0.1098	0.1035	0.02262	0.1853	0.005068	-0.00661	1	0.0919	0.04296	0.1564
movieId_8	0.005078	0.1233	0.1398	0.1497	0.1279	0.005829	0.09197	1	0.07215	-0.01944
movieId_9	-0.05931	0.05253	0.1029	-0.07277	0.08839	0.04757	0.04296	0.07215	1	0.02011
movieId_10	0.02976	0.193	0.02342	0.03346	0.01301	-0.01539	0.1564	-0.01944	0.02011	1