I'm currently studying an item-based collaborative filtering algorithm described in Ul Haq, Raza - Hybrid Recommender System Towards User Satisfaction. I've formulated the algorithm below based on it. I have no problem on steps 1 to 3 but ...
- in step 4 it says there: Set the threshold value n. How can I determine the value of n? Is there a formula for getting the value of it? I already checked it out but there's nothing there.
- in Step 5: Select the K most similar products in M. The same question, how can I compute the value of K?
- Retrieve all the item rated by an active user and put it to Q.
- Isolate the users who have rated both the target item (i) and the items rated by the active user in Q, get the item and put it in R. (co-rated items)
- Calculate the item similarities using the Pearson Correlation Coefficient with all the items (j) in R.
- Set the threshold value n, If the similarity of i and j is greater or equal to n, (sim(i,j) >= n), Then include it in M.
- Select the K most similar products in M.
- Take the weighted average of the users rating on these similar items K.