Real-time
social interactions and
multi-streaming
are two critical features of
live streaming services
. In this paper, we formulate a new fundamental service query,
Social-aware Diverse and Preferred Organization Query (SDSQ)
, that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design
SDSSel
, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose
TDSSel
, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies,
PCP
and
CDP
to boost SDSSel and TDSSel. We further propose a more challenging but practical service query,
Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ)
, a generalization of SDSQ. We design
GPDSel
, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services.
