This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. ; Cataloged from PDF version of thesis report. ; Includes bibliographical references (page 40-44). ; Location based social networks (LBSN) introduce a platform to understand users ‘preference via analyzing the ir check-in history. Such data are being used in the literature for wide variety of location aware recommendation systems. In this thesis, we propose an oval location, time and preference aware restaurant recommendation method by using checkers-in history, user’s current spatial location and current time. In the proposed method, each user’s check-in history is modeled individually to discover the preference etrend by using a logistic function. At the same time, each restaurant’s popularity is calculated using user-restaurant mutual reinforcement learning. The restaurant recommendation scores are computed by considering four key factors, namely, i) user’s preference score ii) the distance of avenue; iii) the time of a day; and iv) popularity of avenue. Each of these key factors is modeled carefully to estimate ear ealistic recommendations core for a restaurant in a given geospatial range. We tested our method using an available data set. The experimental results confirmed the effectiveness of the proposed method. ; Md. Ahsan Habib ; Md. Abdur Rakib ; B. Computer Science and Engineering