Willingness Optimization for Social Group Activity

TL;DR

This paper introduces a novel algorithm for automatically selecting and recommending attendees for social group activities, optimizing user willingness and outperforming manual configurations, with practical implementation on Facebook.

Contribution

It formulates the Willingness Maximization problem and proposes a randomized algorithm with approximation guarantees, improving social group activity planning.

Findings

The algorithm effectively maximizes user willingness for group activities.

Implementation on Facebook shows significant improvement over manual planning.

User study confirms the algorithm's practical effectiveness.

Abstract

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.