Hiroshi Motoda: Which is More Influential, ``Who'' or ``When'' for a User
to Rate in Online Review Site?
to Rate in Online Review Site?
Abstract: At
its heart the act of reviewing is very subjective, but in reality many
factors influence user's decision. This can be called social influence
bias. We pick two factors, ``Who'' and ``When'' and discuss which
factor is more influential when a user posts his/her own rate after
reading the past review scores in an online review system. We show that
a simple model can learn the factor metric quite efficiently from a
vast amount of data that is available in many online review systems and
clarify that there is no universal solution and the influential factor
depends on each dataset. We use a weighted multinomial generative model
that takes account of each user's influence over other users. We
consider two kinds of users: real and virtual, in accordance with the
two factors, and assign an influence metric to each. In the former each
user has its own metric, but in the latter the metric is assigned to
the order of review posting actions (rating). Both metrics are
learnable quite efficiently with a few tens of iterations by
log-likelihood maximization. Goodness of metric is evaluated by the
generalization capability. The proposed method was evaluated and
confirmed effective by five review datasets. Different datasets give
different results. Some dataset clearly indicates that user influence
is more dominant than the order influence while the results are the
other way around for some other dataset, and yet other dataset
indicates that both factors are not relevant. The third one indicates
that the decision is very subjective, i.e., independent of others'
review. We tried to characterize the datasets, but were only partially
successful. For datasets where user influence is dominant, we often
observe that high metric users have strong positive correlations with
three more basic metrics: 1) the number of reviews a user made, 2) the
number of the user's followers who rate the same item, 3) the fraction
of the user's followers who gave the similar rate, but this is not
always true. We also observe that the majority of users is normal
(average) and there are two small groups of users, each with high
metric value and low metric value. Early adopters are not necessarily
influential.