Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior
Abstract
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 highorder 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.