The proposed model defines T interest vectors per user to model user interests as a tensor rather than a single vector. This allows interests like home goods and DVDs to be modeled separately. The key idea is to represent the user part of the model as a tensor. The model is trained using stochastic gradient descent with MapReduce to handle large datasets. Experiments show it works well on real-world music and video recommendation datasets.