Taxonomy Discovery for Personalized Recommendation
Venue
ACM International Conference on Web Search And Data Mining (WSDM) (2014)
Publication Year
2014
Authors
Yuchen Zhang, Amr Ahmed, Vanja Josifovski, Alexander J Smola
BibTeX
Abstract
Personalized recommender systems based on latent factor models are widely used to
increase sales in e-commerce. Such systems use the past behavior of users to
recommend new items that are likely to be of interest to them. However, latent
factor model suffer from sparse user-item interaction in online shopping data: for
a large portion of items that do not have sufficient purchase records, their latent
factors cannot be estimated accurately. In this paper, we propose a novel approach
that automatically discovers the taxonomies from online shopping data and jointly
learns a taxonomy-based recommendation system. Out model is non-parametric and can
learn the taxonomy structure automatically from the data. Since the taxonomy allows
purchase data to be shared between item- s, it effectively improves the accuracy of
recommending tail items by sharing strength with the more frequent items. Ex-
periments on a large-scale online shopping dataset confirm that our proposed model
improves significantly over state-of- the-art latent factor models. Moreover, our
model generates high-quality and human readable taxonomies. Finally, us- ing the
algorithm-generated taxonomy, our model even out- performs latent factor models
based on the human-induced taxonomy, thus alleviating the need for costly manual
taxonomy generation.
