  Nowadays, with the remarkable expansion of the information through the
internet, users prefer to receive the exact information that they need through
some suggestions from their friends or profiles to save their time and money.
Recommend systems based on different algorithms as one of the basic ways to
reach this goal through the internet have been proposed but each of them has
their own advantages and disadvantages. In this study, we have selected and
implemented two approaches which are Collaborative Filtering (CF) and Social
Network Recommendations System (SNRS). Based on some limitations to finding a
dataset which covers friendship, rating and item categories we generated it for
10 categories, 10 items, and 100 users and compared two approaches. We used
Mean Absolute Error (MAE) and accuracy to compare the result of two mentioned
approaches and found that the SNRS method as it is claimed to be improved
version of CF works more efficiency.
