8,178 research outputs found
Scale-Adaptive Group Optimization for Social Activity Planning
Studies have shown that each person is more inclined to enjoy a group
activity when 1) she is interested in the activity, and 2) many friends with
the same interest join it as well. Nevertheless, even with the interest and
social tightness information available in online social networks, nowadays many
social group activities still need to be coordinated manually. In this paper,
therefore, we first formulate a new problem, named Participant Selection for
Group Activity (PSGA), to decide the group size and select proper participants
so that the sum of personal interests and social tightness of the participants
in the group is maximized, while the activity cost is also carefully examined.
To solve the problem, we design a new randomized algorithm, named Budget-Aware
Randomized Group Selection (BARGS), to optimally allocate the computation
budgets for effective selection of the group size and participants, and we
prove that BARGS can acquire the solution with a guaranteed performance bound.
The proposed algorithm was implemented in Facebook, and experimental results
demonstrate that social groups generated by the proposed algorithm
significantly outperform the baseline solutions.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:1305.150
Maximizing Friend-Making Likelihood for Social Activity Organization
The social presence theory in social psychology suggests that
computer-mediated online interactions are inferior to face-to-face, in-person
interactions. In this paper, we consider the scenarios of organizing in person
friend-making social activities via online social networks (OSNs) and formulate
a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by
modeling both existing friendships and the likelihood of new friend making. To
find a set of attendees for socialization activities, HMGF is unique and
challenging due to the interplay of the group size, the constraint on existing
friendships and the objective function on the likelihood of friend making. We
prove that HMGF is NP-Hard, and no approximation algorithm exists unless P =
NP. We then propose an error-bounded approximation algorithm to efficiently
obtain the solutions very close to the optimal solutions. We conduct a user
study to validate our problem formulation and per- form extensive experiments
on real datasets to demonstrate the efficiency and effectiveness of our
proposed algorithm
- …