With the dawn of the information age, the number of choices that each person has to make each day has seen an explosive growth. Every minute, users are bombarded with so many di watching TV, that one live in a state of constant in liberated by it. One ways in which user have now become used to mitigating this constant barrage of incessant information is by the usage of Recommender Systems. These systems are s generate recommendations for items that they may be interested in. Unfortunately,

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... mender systems face a major problem called the "Cold -Start problem", which essentially means that about the user when the user first signs up. Many approaches have been proposed to solve this, but the one this paper shall b taking is to use information from social networks, specifically Twi with good accuracy is proposed here and this understanding of interests can later be updated by observing user actions as they interact with the system. We leverage the power of the cate assign relative weights to entities that a user follows on Twitter. This proposed approach for creating the rating vector for user by mining the Twitter account of the users' is evalu