  Studies show that a person is willing to join a social group activity if the
activity is interesting, and if some close friends also join the activity as
companions. The literature has demonstrated that the interests of a person and
the social tightness among friends can be effectively derived and mined from
social networking websites. However, even with the above two kinds of
information widely available, social group activities still need to be
coordinated manually, and the process is tedious and time-consuming for users,
especially for a large social group activity, due to complications of social
connectivity and the diversity of possible interests among friends. To address
the above important need, this paper proposes to automatically select and
recommend potential attendees of a social group activity, which could be very
useful for social networking websites as a value-added service. We first
formulate a new problem, named Willingness mAximization for Social grOup
(WASO). This paper points out that the solution obtained by a greedy algorithm
is likely to be trapped in a local optimal solution. Thus, we design a new
randomized algorithm to effectively and efficiently solve the problem. Given
the available computational budgets, the proposed algorithm is able to
optimally allocate the resources and find a solution with an approximation
ratio. We implement the proposed algorithm in Facebook, and the user study
demonstrates that social groups obtained by the proposed algorithm
significantly outperform the solutions manually configured by users.
