  Recommender systems based on latent factor models have been effectively used
for understanding user interests and predicting future actions. Such models
work by projecting the users and items into a smaller dimensional space,
thereby clustering similar users and items together and subsequently compute
similarity between unknown user-item pairs. When user-item interactions are
sparse (sparsity problem) or when new items continuously appear (cold start
problem), these models perform poorly. In this paper, we exploit the
combination of taxonomies and latent factor models to mitigate these issues and
improve recommendation accuracy. We observe that taxonomies provide structure
similar to that of a latent factor model: namely, it imposes human-labeled
categories (clusters) over items. This leads to our proposed taxonomy-aware
latent factor model (TF) which combines taxonomies and latent factors using
additive models. We develop efficient algorithms to train the TF models, which
scales to large number of users/items and develop scalable
inference/recommendation algorithms by exploiting the structure of the
taxonomy. In addition, we extend the TF model to account for the temporal
dynamics of user interests using high-order Markov chains. To deal with
large-scale data, we develop a parallel multi-core implementation of our TF
model. We empirically evaluate the TF model for the task of predicting user
purchases using a real-world shopping dataset spanning more than a million
users and products. Our experiments demonstrate the benefits of using our TF
models over existing approaches, in terms of both prediction accuracy and
running time.
