Optimizing Social-Topic Engagement on Social Network and Knowledge Graph

Abstract

Existing research on social networks manifests two crucial criteria to improve activity engagement of users: (1) user interests in the activity topics and (2) opportunities of making new friends with some acquaintances. However, current online platforms still involve massive manual selection for activity attendees and contents without proper recommendations. In this paper, therefore, we formulate a new activity organization problem, named Social Knowledge Group Query (SKGQ), to recommend attendees and topic-related contents simultaneously. We prove that SKGQ is NP-hard and design an approximation algorithm, named Social cOntent Knowledge Exploration (SOKE), to jointly choose the activity attendees and topic-related contents according to social-oriented and topic-oriented strategies. Simulation results manifest that the solution acquired by SOKE is close to the optimal solution and outperforms various baselines.