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Figure 2. Collaborative via Content Recommendation  Recall that the user’s content-based profile contains  weig  hts for the concepts that indicate that a user will  like/dislike an object. When computing Cosine similarity between two profiles, any concept in one profile but not in the other is treated as having a weight of 0 in the other  profi  e. As in collaborative filtering, the prediction made for  an item is determined by a weighted average of all users’  predi profi  ctions for that item, using the similarity between es as the weight. This is demonstrated in Figure (2).

Figure 2 Collaborative via Content Recommendation Recall that the user’s content-based profile contains weig hts for the concepts that indicate that a user will like/dislike an object. When computing Cosine similarity between two profiles, any concept in one profile but not in the other is treated as having a weight of 0 in the other profi e. As in collaborative filtering, the prediction made for an item is determined by a weighted average of all users’ predi profi ctions for that item, using the similarity between es as the weight. This is demonstrated in Figure (2).