As the amount and complexity of data keeps increasing in data warehouses, their exploration for analytical purposes may be hin- dered. Recommender systems have grown very popular on the Web with sites like Amazon, Net ix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to sug- gest data warehouse queries and help an analyst pursue its exploration. In this paper, we present a personalized query expansion component which suggests measures and dimensions to iteratively build consistent queries over a data warehouse. Our approach leverages (a) semantics de ned in multi-dimensional domain models, (b) collaborative usage statistics de- rived from existing repositories of Business Intelligence documents like dashboards and reports and (c) preferences de ned in a user pro le. We nally present results obtained with a prototype implementation of an interactive query designer.