ABSTRACT In recent years, recommendation engines have gained a lot of success on many online giant commerce and entertainment platforms. Recommending similar products that users will like using the user's past behavior is a challenging problem especially because of the unpredictable nature of people’s likes and dislikes. It also involves a guess about the future based on something that the user has never seen which makes it that much harder to predict mainly because people’s tastes change all the time. What we can do is try to estimate those values as best as we can using the Adamic Adar measure by creating nodes and finding similarities between those nodes. Unlike most of the existing recommendation systems that use either collaborative filtering or content-based filtering to generate recommendations, this paper explores a slightly different approach by creating node pairs consisting of common neighbors but with a lower degree and calculating the Adamic Adar Coefficient of those two nodes. Adamic Adar Coefficient is a measure that is used to calculate the closeness of two nodes based on their common neighbor. This paper describes a recommendation engine built that can be used to predict similar items when a user is browsing an eCommerce, music or movie platform based on the user’s behavior. It takes in the item’s features such as description, price, title, ratings, etc., and creates nodes for each word to find commonalities between those nodes. It then generates nodes with the highest Adamic Adar Coefficient which will result in the items that are close in characteristics to the currently viewed item by the user. Adamic Adar Coefficient: If I and J are two nodes, the Adamic Adar Coefficient of I and J would be calculated as Whereas N(node) is a function that returns the set of neighboring nodes ; Comment: 17 pages