The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data
