The following table displays the ratings of 10 customers for 6 products. Ratings are between 1...

 

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The following table displays the ratings of 10 customers for 6 products. Ratings are between 1 and 10. White cells indicate unknown ratings. Products I 2 3 4 5 6 1 5 8 7 2 3 9 1 5 6 65 4 8 LA 00 5 Customers 5 www 3 2 3 7 4 3 6 4 2 3 7 8 7 5 4 9 9 53 4 10 5 Cand 2 a. Apply User-User collaborative filtering to predict the rating of user 5 for product 2. Use Pearson correlation coefficient as the similarity measure and consider a neighborhood size of N-3. b. Apply Item-Item collaborative filtering to predict the rating of user 5 for product 2. Use Cosine similarity as the measure and consider a neighborhood size of |N| = 2. The following table displays the ratings of 10 customers for 6 products. Ratings are between 1 and 10. White cells indicate unknown ratings. Products I 2 3 4 5 6 1 5 8 7 2 3 9 1 5 6 65 4 8 LA 00 5 Customers 5 www 3 2 3 7 4 3 6 4 2 3 7 8 7 5 4 9 9 53 4 10 5 Cand 2 a. Apply User-User collaborative filtering to predict the rating of user 5 for product 2. Use Pearson correlation coefficient as the similarity measure and consider a neighborhood size of N-3. b. Apply Item-Item collaborative filtering to predict the rating of user 5 for product 2. Use Cosine similarity as the measure and consider a neighborhood size of |N| = 2.

Answer rating: 100% (QA)

To solve this problem well apply both UserUser and ItemItem collaborative filtering techniques a Use... View the full answer