Consumers today have high standards for determining which products meet their specific needs. As such, a satisfying shopping experience is defined as one in which consumers can quickly find what they are looking for. Electronic commerce, or e-commerce for short, has met these high standards, allowing consumers to enter search terms to narrow down an online retailer's inventory to the item they are looking for, then place orders from the comfort of their home. However, online retailers must be quick to get consumers the products they want; If the customer feels that their search is not going well, they will simply leave the online retailer to complete a transaction with a competing retailer. This race to satisfy consumer needs has given rise to personalized recommendations, which are programmed suggestions of products that the online retailer believes consumers should consider purchasing. As a result, consumers were surprised and concerned about their privacy, wondering what information companies are using to make these new recommendations. However, consumers should not worry about their privacy; rather, they should continue to be interested in these personalized recommendations in order to broaden their search and get closer to a product that interests them, thus leading them to have a better online shopping experience. The privacy debate for generating personalized recommendations takes two perspectives: consumers and online businesses. On the one hand, online consumers feel that companies are invading their privacy by using sensitive information to generate personalized product recommendations. On the other hand, companies claim their personalized product recommendations ... middle of paper ... don't click. Proceedings of the 15th International Conference on Intelligent User Interfaces (2010): 31-40.Machanavajjhala, Ashwin, Aleksandra Korolova and Atish Das Sarma. “Personalized social recommendations: precise or private? » Proceedings of the VLDB Endowment 4.7 (2011): 440-450. Shardanand, Upendra and Pattie Maes. “Social information filtering: algorithms to automate “word of mouth”. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (1995): 210-217. Shepitsen, Andriy, et al. “Personalized recommendation in social tagging systems using hierarchical clustering.” Proceedings of the 2008 ACM Conference on Recommender Systems (2008): 259-266. Zhang, Zhiyong, and Olfa Nasraoui. “Mining search engine query logs for query recommendations based on social filtering.” Applied soft computing 8.4 (2008): 1326-1334.