When Social Influence Meets Item Inference
Hui-Ju Hung, Pennsylvania State University; Hong-Han Shuai, Academia Sinica; De-Nian Yang*, Academic Sinica; Liang-Hao Huang, Academia Sinica; Wang-Chien Lee, The Pennsylvania State University; Jian Pei, Simon Fraser University; Ming-Syan Chen, National Taiwan University
Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the eﬀect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both eﬀects of social inﬂuence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both eﬀects in the form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an eﬃcient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diﬀusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the eﬀectiveness and eﬃciency of the pro-posed model and algorithms over baselines.
Filed under: Graph Mining and Social Networks