Abstract
Massive amounts of data are available on social websites, therefore finding the suitable item is a challenging issue. According to recent social statistics, we have more than 930 million people are using WhatsApp with more than 340 million active daily users and 955 million people who access Facebook daily with an average daily photo uploads up to 325 million. The approach presented in this paper employs the collaborative tagging accumulated by huge number of users to improve social media recommendation. Our approach has two phases, in the first phase, we compute the tag-item weight model and in the second phase, we compute the user-tag preference model. After that we employ the two models to find the suitable items tailored to the user's preferences and recommend the items with the highest score. Also our model can compute the tag score and suggest the tags with the highest weight to the user according to their preferences. The experiment results performed on Flicker and MovieLens prove that our approach is capable to improve the social media recommendation.