  Between matrix factorization or Random Walk with Restart (RWR), which method
works better for recommender systems? Which method handles explicit or implicit
feedback data better? Does additional information help recommendation?
Recommender systems play an important role in many e-commerce services such as
Amazon and Netflix to recommend new items to a user. Among various
recommendation strategies, collaborative filtering has shown good performance
by using rating patterns of users. Matrix factorization and random walk with
restart are the most representative collaborative filtering methods. However,
it is still unclear which method provides better recommendation performance
despite their extensive utility.
  In this paper, we provide a comparative study of matrix factorization and RWR
in recommender systems. We exactly formulate each correspondence of the two
methods according to various tasks in recommendation. Especially, we newly
devise an RWR method using global bias term which corresponds to a matrix
factorization method using biases. We describe details of the two methods in
various aspects of recommendation quality such as how those methods handle
cold-start problem which typically happens in collaborative filtering. We
extensively perform experiments over real-world datasets to evaluate the
performance of each method in terms of various measures. We observe that matrix
factorization performs better with explicit feedback ratings while RWR is
better with implicit ones. We also observe that exploiting global popularities
of items is advantageous in the performance and that side information produces
positive synergy with explicit feedback but gives negative effects with
implicit one.
