Abstract:
Recommender Systems typically use techniques
from collaborative filtering which recommend items
that users with similar preferences have liked in the
past and, also predict new rating by averaging
ratings between pairs of similar users or items.
Predictions come from three sources: predictions
based on ratings of the same item by other users,
predictions based on different item ratings made by
the same user, and ratings predicted based on data
from other but similar users rating other but similar
items. In this system, we use prediction algorithms to
provide users with items that match their interests
based on collaborative filtering (CF) approach. Our
system use similarity measures between users, and
also between items from a single rating criteria .We
provide analysis of user-based and item-based
prediction algorithms. The accuracy of the
algorithms is compared by Mean Absolute Error.
