Socio-Spatial Group Queries for Impromptu Activity Planning

TL;DR

This work defines and tackles MR(G)Q, a problem that jointly selects a socially cohesive group of exactly $p$ attendees and a rally location from a set $Q$ to minimize total spatial distance while enforcing a social familiarity constraint $k$ and a spatial radius $t$. It proves NP-hardness and inapproximability, providing ILP formulations for baseline cases and introducing two novel algorithms: SSGS/SSGMerge for SSGQ (single location) and MAGS for MRGQ (multiple rally points). MAGS combines indexing (R-Tree for attendees, BallTree for locations), distance-ordering (SRDO and APDO), and multiple distance-pruning strategies (OTDP, ITDP, ALDP) to efficiently prune the search space and guarantee optimality in many practical regimes. Extensive experiments, including a 206-person user study and large real datasets, show substantial speedups and superior solution quality over ILP baselines and competitive methods, validating the approach for real-time social planning on platforms like Facebook and Groupon-like services. The work advances practical, scalable, socially aware location planning with clear implications for promoting coordinated, proximity-aware group activities in LBSN ecosystems.

Abstract

The development and integration of social networking services and smartphones have made it easy for individuals to organize impromptu social activities anywhere and anytime. Main challenges arising in organizing impromptu activities are mostly due to the requirements of making timely invitations in accordance with the potential activity locations, corresponding to the locations of and the relationship among the candidate attendees. Various combinations of candidate attendees and activity locations create a large solution space. Thus, in this paper, we propose Multiple Rally-Point Social Spatial Group Query (MRGQ), to select an appropriate activity location for a group of nearby attendees with tight social relationships. Although MRGQ is NP-hard, the number of attendees in practice is usually small enough such that an optimal solution can be found efficiently. Therefore, we first propose an Integer Linear Programming optimization model for MRGQ. We then design an efficient algorithm, called MAGS, which employs effective search space exploration and pruning strategies to reduce the running time for finding the optimal solution. We also propose to further optimize efficiency by indexing the potential activity locations. A user study demonstrates the strength of using MAGS over manual coordination in terms of both solution quality and efficiency. Experimental results on real datasets show that our algorithms can process MRGQ efficiently and significantly outperform other baseline algorithms, including one based on the commercial parallel optimizer IBM CPLEX.

Figures (22)

• Figure 1: Finding friends for impromptu social activity.
• Figure 2: Example of ordering strategies.
• Figure 3: Example of SSGMerge.
• Figure 4: Example of SFGP.
• Figure 5: Comparisons of R-Tree and BallTree.

Theorems & Definitions (22)

• Theorem 1
• Proof 1
• Theorem 2
• Proof 2
• Theorem 3
• Proof 3
• Lemma 1
• Proof 4
• Lemma 2
• Proof 5