  We introduce a novel latent vector space model that jointly learns the latent
representations of words, e-commerce products and a mapping between the two
without the need for explicit annotations. The power of the model lies in its
ability to directly model the discriminative relation between products and a
particular word. We compare our method to existing latent vector space models
(LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank
setting. Our latent vector space model achieves its enhanced performance as it
learns better product representations. Furthermore, the mapping from words to
products and the representations of words benefit directly from the errors
propagated back from the product representations during parameter estimation.
We provide an in-depth analysis of the performance of our model and analyze the
structure of the learned representations.
