When Social Influence Meets Item Inference

TL;DR

This paper introduces a new model and algorithms for seed selection in viral marketing that jointly consider social influence and product inference effects, validated through comprehensive experiments.

Contribution

It proposes the Social Item Graph model, formulates the seed selection problem, and develops efficient algorithms with theoretical guarantees and a data-driven hyperedge inference framework.

Findings

The proposed HAG algorithm outperforms baselines in effectiveness.

SIG model accurately captures social influence and item inference effects.

Experimental results demonstrate the efficiency and effectiveness of the approach.

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 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.