Abstract: Although people can use Web search engines to explore scenic spots for traveling, they often find it very difficult to discover the sighting sites which match their personalized need well. Tour recommendation systems can be used to solve the issue. A good tour recommendation system should be able to provide personalized recommendation and take the time and cost factors into account. Furthermore, our investigation shows that often a user u will consider the distance between her/his habitual residence and the tour destination when she/he makes her/his travel plan. It is because that the travel distance reflects the effect of time and cost indirectly. Therefore, we propose a distance-aware tour recommendation model, named GeoPMF (geographical probabilistic matrix factorization), which is developed based on the Bayesian model and PMF (probabilistic matrix factorization). The main idea of GeoPMF is that for each user we try to get a most preferred travel distance span by mining her past tour records. Then we use it as a kind of weight factors added into the traditional PMF model. Experiments on travel data of Ctrip show that, our new method can decrease RMSE (root mean square error) nearly 10% compared with some baseline methods. And when compared with the traditional PMF model, the average decline on RMSE is nearly 3.5% in virtue of the distance factor.