  We propose to augment rating based recommender systems by providing the user
with additional information which might help him in his choice or in the
understanding of the recommendation. We consider here as a new task, the
generation of personalized reviews associated to items. We use an extractive
summary formulation for generating these reviews. We also show that the two
information sources, ratings and items could be used both for estimating
ratings and for generating summaries, leading to improved performance for each
system compared to the use of a single source. Besides these two contributions,
we show how a personalized polarity classifier can integrate the rating and
textual aspects. Overall, the proposed system offers the user three
personalized hints for a recommendation: rating, text and polarity. We evaluate
these three components on two datasets using appropriate measures for each
task.
