When social influence meets item inference

Hui Ju Hung, Hong Han Shuai, De Nian Yang, Liang Hao Huang, Wang Chien Lee, Jian Pei, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages915-924
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'When social influence meets item inference'. Together they form a unique fingerprint.

  • Cite this

    Hung, H. J., Shuai, H. H., Yang, D. N., Huang, L. H., Lee, W. C., Pei, J., & Chen, M. S. (2016). When social influence meets item inference. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 915-924). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939758