I am trying to understand how can I calculate the similarity between
itemid. Here is the user-based table.The table didn't have rating /score information.
userid | itemid1 | itemid2 | itemid3 ... | itemid10000
1 | 1 | 0 | 1 | 1
2 | 0 | 1 | 1 | 1
3 | 0 | 0 | 0 | 1
1)Can I still use KNN method (like
manhattan distance or
euclidean distance) and
cosine similarity method to calculate the similarity score? If so,how can I get these scores as vector matrix.
2)Suppose we have itemid >
100,000,000, so the table is very sparse. So do we have any method to control sparse data to deal with collaborative filtering problems.