Personalized recommendations are designed to provide recommendations with customized preferences of the user based on the historical data on the desired item. Classic recommendation systems suggest the items only depend on the informational relationship between the user and items. Such systems lack the awareness of contextual information of the user. The significance of the contextual information focuses on the associated information of the users situation while interacting with the item. Contextual information includes location, profiles, weather, people, and any information relevant to the circumstances. Integrating the contextual awareness to personalized recommendation requires a pre-filtering process, which follows dimensionality reduction for the selection of data that according to contextual conditions to generate relevant suggestions. The contextually enriched model with pre-filtering improves the personalized recommendation systems, supports the user in decision making by providing useful recommendations, and yields high statistically significant outcomes.