Hello All,
In item-based CF algorithms, when we predict the rating P_{u, i} of some
user 'u' for some item 'i', shall we consider all available items j, whose
similarities with item 'i' are not zero, or shall we only consider those
items whose have been both rated by user u and their similarities with item
i are not zero. This will affect the normalization part as we need to sum up
all those considered similarity scores.
I am reading the following paper,
Item-Based Collaborative Filtering Recommendation Algorithms (
www.grouplens.org/papers/pdf/www10_sarwar.pdf)
The normalization part is in the middle left of page 5.
Thank you very much,
Guohua
