Big data is an emerging field plays a valuable role in the extraction of information from raw data. It has found its applications in areas such as predictive analytics, healthcare analytics, financial analytics, and retail analytics and so on. The enormous growth of the Internet has become a source for availability of the huge volume of data online. It is difficult to find out the necessary information from huge data within a short period. The availability of enormous data craves the need of an information filtering system and this information filtering systems are capable of providing the required data to users. The rapid growth of big data lays the path to recommendation systems. A recommendation system is an information filtering tool which has more impacts in day to day life of everyone and also redefines our lives. Recommendation systems provide suggestions based on user preferences, requirements, and interests. The reviews and rating values given by people are used to answer similar interest queries with predictions and suggestions. Reviews and Feedback play a key role in the decision-making process. People share their experiences in the form of feedback, ratings, and reviews and so on. If a user wants to visit a location and if he does not have prior knowledge of it, then he may use reviews and feedback given by others who visited the location already. It is not possible for a user to go through huge volumes of reviews and sometimes it may mislead the user to take wrong decisions if he goes by the review given by a person with a contrasting taste. In such cases, Recommendation systems are needed, which helps users in the decision-making process. In most of the existing methods, they used Point of Interest (POI) of users to recommend the locations. The main objective is to develop a Personalized Location Recommendation System, which will recommend the locations to users using Probability and Proximity. Our model uses Probability and Proximity measures to recommend the locations.