Recommendation systems are successful because they solve ‌sparse data, which occurs when there is inadequate knowledge of the user’s choices for all objects in the database. This scarcity issue develops because of the massive volume of content accessible via platforms such as Amazon or YouTube. For example, if a platform contains 1000 items but an individual has only engaged with a few, forecasting their choices for the rest is difficult. To get around this, recommendation systems use a variety of tactics. They encourage users to rate goods, collecting additional data for understanding individual preferences. By assessing these scores, the algorithm can deduce bigger patterns and provide suggestions even when information is insufficient.