Scale-Adaptive Group Optimization for Social Activity Planning

TL;DR

This paper introduces a novel optimization problem for social activity planning, proposing a randomized algorithm that effectively selects participants to maximize interest and social tightness while minimizing costs, validated through Facebook implementation.

Contribution

It formulates the PSGA problem and develops BARGS, a new randomized algorithm with performance guarantees for optimal group selection in social activities.

Findings

BARGS outperforms baseline solutions in experiments.

Implemented in Facebook, demonstrating practical effectiveness.

Maximizes participant interest and social tightness while controlling costs.

Abstract

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.