2. The following table shows the rating of users u; to item mj. Find all the predicted ratings for items which have not yet been rated by users using item-based collaborative filtering approach. user ul ul u2 u2 item ml m3 ml m2 ml m2 m3 m2 m3 rating 2 3 5 2 3 3 1 2 2 u3 u3 u3 u4 u4 Use the cosine similarity measures for measuring the similarity between items and the following equation for prediction. pred(u,p) Emeratedltem(u) sim(m,p) * lum Emeratedltem(u) sim(m,p) where: sim(m, p) is the similarity between item m and the predicted item p Tum is the rating given by user u on item m. (20M) pred(u,p) Lierateditem(u) sim(i,p) * rui LieratedItem(u) sim(i,p) ) 2. The following table shows the rating of users u; to item mj. Find all the predicted ratings for items which have not yet been rated by users using item-based collaborative filtering approach. user ul ul u2 u2 item ml m3 ml m2 ml m2 m3 m2 m3 rating 2 3 5 2 3 3 1 2 2 u3 u3 u3 u4 u4 Use the cosine similarity measures for measuring the similarity between items and the following equation for prediction. pred(u,p) Emeratedltem(u) sim(m,p) * lum Emeratedltem(u) sim(m,p) where: sim(m, p) is the similarity between item m and the predicted item p Tum is the rating given by user u on item m. (20M) pred(u,p) Lierateditem(u) sim(i,p) * rui LieratedItem(u) sim(i,p) )