  Nowadays, we have large amounts of online items in various web-based
applications, which makes it an important task to build effective personalized
recommender systems so as to save users' efforts in information seeking. One of
the most extensively and successfully used methods for personalized
recommendation is the Collaborative Filtering (CF) technique, which makes
recommendation based on users' historical choices as well as those of the
others'. The most popular CF method, like Latent Factor Model (LFM), is to
model how users evaluate items by understanding the hidden dimension or factors
of their opinions. How to model these hidden factors is key to improve the
performance of recommender system. In this work, we consider the problem of
hotel recommendation for travel planning services by integrating the location
information and the user's preference for recommendation. The intuition is that
user preferences may change dynamically over different locations, thus treating
the historical decisions of a user as static or universally applicable can be
infeasible in real-world applications. For example, users may prefer chain
brand hotels with standard configurations when traveling for business, while
they may prefer unique local hotels when traveling for entertainment. In this
paper, we aim to provide trip-level personalization for users in
recommendation.
