  Giving or recommending appropriate content based on the quality of experience
is the most important and challenging issue in recommender systems. As
collaborative filtering (CF) is one of the most prominent and popular
techniques used for recommender systems, we propose a new clustering-based CF
(CBCF) method using an incentivized/penalized user (IPU) model only with
ratings given by users, which is thus easy to implement. We aim to design such
a simple clustering-based approach with no further prior information while
improving the recommendation accuracy. To be precise, the purpose of CBCF with
the IPU model is to improve recommendation performance such as precision,
recall, and $F_1$ score by carefully exploiting different preferences among
users. Specifically, we formulate a constrained optimization problem, in which
we aim to maximize the recall (or equivalently $F_1$ score) for a given
precision. To this end, users are divided into several clusters based on the
actual rating data and Pearson correlation coefficient. Afterwards, we give
each item an incentive/penalty according to the preference tendency by users
within the same cluster. Our experimental results show a significant
performance improvement over the baseline CF scheme without clustering in terms
of recall or $F_1$ score for a given precision.
