Interactive database exploration is a key task in information mining. Relational databases have been long used as a critical infrastructure component to access and analyze large volumes of data in a variety of applications, including ad-hoc analytics over big data, large-scale data warehouses that support business-intelligence tools, and services for scientific-data exploration. To aid the users of such databases, we developed the QueRIE system for personalized query recommendations. Similarly to traditional recommender systems, QueRIE continuously monitors the user's querying behavior and finds matching patterns in the system's query log, identifying " similar" users. Subsequently, these users and their queries are being used to recommend queries that the current user may find useful. We have previously shown that when employing different neighborhood-based collaborative filtering techniques, there exists a trade-off between computational efficiency and accuracy. In this paper we extend our previous work on the QueRIE framework, to address scalability, the most desirable characteristic of applications that rely on the mining of big data. Latent factor collaborative filtering models have been shown to address the scalability problem in traditional rating-based recommender systems, without much compromise to the recommender system's accuracy. In this work, we explore the use of latent factor models when, instead of ratings, the input consists of database-query log data. We show through experimentation that, as in the case of rating-based recommender systems, such techniques offer both scalability and prediction accuracy in the database query recommendations domain, outperforming the neighborhood-based approaches.