Aiming at the sensitivity of most users’consumption habits to the geographical location and the "long tail effect" in the recommendation process, a collaborative filtering algorithm is proposed combining the location information and the item popularity. In this study, two improvements are made to the conventional collaborative filtering algorithm. At first, a new calculating method of user similarity based on the geographic location is proposed by combining the preference of user interest and the position preference. Then, in predicting, the recommended expectations of the popular items and the long-tailed items are reasonably adjusted by introducing the popularity of goods weight. The Foursquare data set is used as the experimental data set in this work, and experiments are compared with related algorithms. The experimental results show that the improved algorithm can effectively improve the precision and the diversity of the recommended results.