Hi,
I tried to calculate SVD based recommendation by: 1) Calculate A'A 2) Calculate V_k by using Lanczos to get top k eigenvectors of A'A 3) Calculate preferences for each user by Vector usersPreference = V_k.timesSquared(row of A); 4) Calculate 20 recommendation for each user by selecting the top 20 preference from usersPreference The number of users and items is between 100 000 and 300 000. In my experience Step 3) scales linearly by the number of users. Each job runs fast, but I have ~100 000 jobs. This slows down the whole calculation. Is the approach wrong that I use? Or can you suggest something to make this faster? Regards, Laszlo
