  Learning a good representation of text is key to many recommendation
applications. Examples include news recommendation where texts to be
recommended are constantly published everyday. However, most existing
recommendation techniques, such as matrix factorization based methods, mainly
rely on interaction histories to learn representations of items. While latent
factors of items can be learned effectively from user interaction data, in many
cases, such data is not available, especially for newly emerged items.
  In this work, we aim to address the problem of personalized recommendation
for completely new items with text information available. We cast the problem
as a personalized text ranking problem and propose a general framework that
combines text embedding with personalized recommendation. Users and textual
content are embedded into latent feature space. The text embedding function can
be learned end-to-end by predicting user interactions with items. To alleviate
sparsity in interaction data, and leverage large amount of text data with
little or no user interactions, we further propose a joint text embedding model
that incorporates unsupervised text embedding with a combination module.
Experimental results show that our model can significantly improve the
effectiveness of recommendation systems on real-world datasets.
