The information explosion on the Internet has raised the need for effective handling of information overload. Recommender systems provide personalized content and service recommendations to users based on the use of machine learning algorithms. In this manner, the accuracy and performance of a recommender system, through which big datasets including unstructured data like images and text are formulated, is bettered. This has brought better understanding to the users and made recommendations more relevant. Since all these are advanced technologies, recommender systems are very likely to form an important part in wide applications across social networking, news, and education. However, all these will be developed and accepted with due consideration of user privacy and data security at the forefront. Indeed, striking the balance between making innovation happen and privacy protection may be the way to mandate fostering user trust and ensure a sustainable future for recommender systems.