  Recommendation systems are an important units in today's e-commerce
applications, such as targeted advertising, personalized marketing and
information retrieval. In recent years, the importance of contextual
information has motivated generation of personalized recommendations according
to the available contextual information of users.
  Compared to the traditional systems which mainly utilize users' rating
history, review-based recommendation hopefully provide more relevant results to
users. We introduce a review-based recommendation approach that obtains
contextual information by mining user reviews. The proposed approach relate to
features obtained by analyzing textual reviews using methods developed in
Natural Language Processing (NLP) and information retrieval discipline to
compute a utility function over a given item. An item utility is a measure that
shows how much it is preferred according to user's current context.
  In our system, the context inference is modeled as similarity between the
users reviews history and the item reviews history. As an example application,
we used our method to mine contextual data from customers' reviews of movies
and use it to produce review-based rating prediction. The predicted ratings can
generate recommendations that are item-based and should appear at the
recommended items list in the product page. Our evaluations suggest that our
system can help produce better prediction rating scores in comparison to the
standard prediction methods.
