Location aware mobile devices have increased theavailability of user trajectory information making point ofinterest recommenders a popular service on mobile devices. However, one of the main challenges in this area is sparsity ofthe historical trajectory data. So far, most of the recommendersystems take users' historical trajectory information into considerationto recommend different places. Web interactionsreveal rich information on the user interests, and hence arecommender system should take into consideration such data. In this study, we present a model that combines andassociates users interest/taste information, obtained from theirweb interactions together with location information obtainedfrom the Open Street Map (OSM). Then, we combine thisinformation with the users' real time trajectory information(longitude, latitude and timestamp) to present a list of recommendedpoints of interest close to the current location.