  In this work, we present an approach for mining user preferences and
recommendation based on reviews. There have been various studies worked on
recommendation problem. However, most of the studies beyond one aspect user
generated- content such as user ratings, user feedback and so on to state user
preferences. There is a prob- lem in one aspect mining is lacking for stating
user preferences. As a demonstration, in collaborative filter recommendation,
we try to figure out the preference trend of crowded users, then use that trend
to predict current user preference. Therefore, there is a gap between real user
preferences and the trend of the crowded people. Additionally, user preferences
can be addressed from mining user reviews since user often comment about
various aspects of products. To solve this problem, we mainly focus on mining
product aspects and user aspects inside user reviews to directly state user
preferences. We also take into account Social Network Analysis for cold-start
item problem. With cold-start user problem, collaborative filter algorithm is
employed in our work. The framework is general enough to be applied to
different recommendation domains. Theoretically, our method would achieve a
significant enhancement.
