a e Q4 (15 points) We have the following user rating matrix. b d f g h A 4. 5 5 1 3 2 B 3 4 3 1 2 1 2 1 3 4 5 3 A, B, and C are users. a, b, ..., h are items. Compute the following from the rating matrix. B, 4.1 (5 points) Treat the matrix as binary where observed ratings are regarded as 1 and missing ratings are regarded as 0. Compute the Jaccard similarity between each pair of users. 4.2 (5 points) Treat missing ratings as 0 and compute the cosine similarity between each pair of users. The cosine similarity between two vectors x and y is cos(x,y) |x[2]ylz: 4.3 (5 points) Use the cosine similarity defined in 4.2 to perform user-based collaborative filtering to predict the rating of user A on item c. a e Q4 (15 points) We have the following user rating matrix. b d f g h A 4. 5 5 1 3 2 B 3 4 3 1 2 1 2 1 3 4 5 3 A, B, and C are users. a, b, ..., h are items. Compute the following from the rating matrix. B, 4.1 (5 points) Treat the matrix as binary where observed ratings are regarded as 1 and missing ratings are regarded as 0. Compute the Jaccard similarity between each pair of users. 4.2 (5 points) Treat missing ratings as 0 and compute the cosine similarity between each pair of users. The cosine similarity between two vectors x and y is cos(x,y) |x[2]ylz: 4.3 (5 points) Use the cosine similarity defined in 4.2 to perform user-based collaborative filtering to predict the rating of user A on item c.