TY - GEN
T1 - When social influence meets item inference
AU - Hung, Hui Ju
AU - Shuai, Hong Han
AU - Yang, De Nian
AU - Huang, Liang Hao
AU - Lee, Wang Chien
AU - Pei, Jian
AU - Chen, Ming Syan
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/13
Y1 - 2016/8/13
N2 - 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 proposed model and algorithms over baselines.
AB - 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 proposed model and algorithms over baselines.
UR - http://www.scopus.com/inward/record.url?scp=84984972938&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984972938&partnerID=8YFLogxK
U2 - 10.1145/2939672.2939758
DO - 10.1145/2939672.2939758
M3 - Conference contribution
AN - SCOPUS:84984972938
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 915
EP - 924
BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Y2 - 13 August 2016 through 17 August 2016
ER -