Abstract
Activity organization query for Location-Based Social Networks is an important research problem, which aims at selecting a suitable group of people relevant to the query user and activity according to their social and spatial information. However, current activity organization queries mainly consider simplistic and direct relationships to measure the relevance. Although Heterogeneous Information Networks capture complex relationships by meta-structures, the relevance is seldom measured from both social and spatial aspects and does not take the distinctiveness of meta-structure into account. To fill this gap, we first propose a new relevance measurement, named SIMER to more accurately measure the connection strength. Then, we formulate a new query, named MHS2Q, which considers the social and spatial factors as well as the distinctiveness of meta-structures. Furthermore, we extend the MHS2Q to Subsequent MHS2Q, to consider a series of queries with varying spatial constraints. We design an efficient algorithm MS2MU to answer the (Subsequent) MHS2Q, which exploits a new index structure named d-Table to boost the computation for subsequent queries, and a pruning strategy, MSR-pruning to avoid unnecessary computation. Experiments on real LBSNs show that MS2MU is more effective to retrieve a social group that is both relevant and socially tight to the query.