Consumers today have high standards for determining which products meet their specific needs. Therefore, a satisfying shopping experience is one in which consumers can quickly find what they are looking for. Electronic commerce, or e-commerce for short, has addressed these high standards, allowing consumers to enter search terms to narrow an online retailer's inventory to the item they're looking for and then place orders from the comfort of their own home. However, online retailers must be quick to match consumers with 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 meet consumer needs has given rise to personalized recommendations, which are programmed suggestions for products that the online retailer believes consumers should consider purchasing. As a result, consumers have been 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 interact with these personalized recommendations to expand their search and get closer to a product of their interest, thus leading them to have a better online shopping experience. The privacy debate for generating personalized recommendations takes two points of view: consumers and online companies. On the one hand, online consumers believe that companies invade their privacy by using sensitive information to generate personalized product recommendations. On the other hand, companies claim that their personalized products recommend… middle of the paper… don’t click behavior.” Proceedings of the 15th International Conference on Intelligent User Interfaces (2010): 31-40.Machanavajjhala, Ashwin, Aleksandra Korolova, and Atish Das Sarma. “Personalized social recommendations: accurate or private?” Proceedings of the VLDB Endowment 4.7 (2011): 440-450. Shardanand, Upendra and Pattie Maes. “Social Information Filtering: Algorithms for Automating '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 Recommendersystems (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.