  The rating score prediction is widely studied in recommender system, which
predicts the rating scores of users on items through making use of the
user-item interaction information. Besides the rating information between users
and items, lots of additional information have been employed to promote
recommendations, such as social relation and geographic location. Expenditure
information on each transaction between users and items is widely available on
e-commerce websites, often appearing next to the rating information, while
there is seldom study on the correlation between expenditures and rating
scores. In this paper, we first study their correlations in real data sets and
propose the expenditure aware rating prediction problem. From the data sets
crawled from a well-known social media platform Dianping in China, we find some
insightful correlations between expenditures and rating scores: 1) transactions
or experiences with higher expenditures usually lead to higher rating scores;
2) when the real expenditures are higher than users' normal spending behavior,
the users usually give higher scores; and 3) there are multiple grades of
expenditure behaviors. Based on these three observations, we propose an
Expenditure ware RatingPrediction method (EARP), based on low-rank matrix
factorization, to effectively incorporate the expenditure information.
Extensive experiments on five real data sets show that EARP not only always
outperforms other state-of-the-art baselines but also discovers the latent
characteristics of users and businesses.
