Datasets gathered from sensor networks often suffer from a significant fraction of missing data, due to issues such as communication and sensor interference, power depletion, and hardware failure. Many standard data analysis tools such as classification engines, time-sequence pattern analysis modules, and statistical tools are ill-equipped to deal with missing values — hence, there is a vital need for highly-accurate techniques for imputing missing readings prior to analysis. This paper presents novel imputation methods that take a "recommendation systems" view of the problem: the sensors and their readings at each time step are viewed as products and user product ratings, with the goal of estimating the missing ratings. Sensor readings differ from product ratings, however, in that the former exhibit high correlation in both time and space. To incorporate this property, we modify the widely successful matrix factorization approach for recommendation systems to model inter-sensor and intra-sensor correlations and learn latent relationships among these dimensions. We evaluate the approach using two sensor network datasets, one indoor and one outdoor, and two imputation scenarios, corresponding to intermittent readings and failed sensors. Next, we consider sensor networks with multiple sensor types at each node. We present two techniques for extending our model to account for possible correlations among sensor types (e.g., temperature and humidity) with promising results. Finally, we study how the imputed values affect the result of data analysis. We consider a popular data analysis task — building regression-based prediction models — and show that, compared to prior approaches for imputation, our method leads to a much higher quality prediction model.