Paper Info
Title
When Social Influence Meets Item Inference
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.
Year
DOI
Venue
2016
10.1145/2939672.2939758
KDD
Keywords
Field
DocType
viral marketing,product recommendation,frequent pattern
Data mining,Viral marketing,Social network,Computer science,Inference,Baseline (configuration management),Social influence,Artificial intelligence,Statistical inference,Maximization,Machine learning,Computation
Conference
Citations 
PageRank 
References 
8
0.44
19
Authors
7
Name
Order
Citations
PageRank
Hui-Ju Hung1484.29
Hong-Han Shuai210024.80
De-Nian Yang358666.66
Liang-Hao Huang4855.32
Wang-Chien Lee55765346.32
Jian Pei619002995.54
Ming Chen765071277.71