Carnegie Mellon University
Latent Topic Analysis for Predicting Group Purchasing Behavior on.pdf (2.25 MB)

Latent Topic Analysis for Predicting Group Purchasing Behavior on the Social Web

Download (2.25 MB)
journal contribution
posted on 2013-07-01, 00:00 authored by Feng-Tso Sun, Martin Griss, Ole J Mengshoel, Yi-Ting Yeh
Group-deal websites, where customers purchase products or services in groups, are an interesting phenomenon on the Web. Each purchase is kicked off by a group initiator, and other customers can join in. Customers form communities with people with similar interests and preferences (as in a social network), and this drives bulk purchasing (similar to online stores, but in larger quantities per order, thus customers get a better deal). In this work, we aim to better understand what factors in influence customers' purchasing behavior for such social group-deal websites. We propose two probabilistic graphical models, i.e., a product-centric inference model (PCIM) and a group-initiator-centric inference model (GICIM), based on Latent Dirichlet Allocation (LDA). Instead of merely using a customers' own purchase history to predict purchasing decisions, these two models include other social factors. Using a lift curve analysis, we show that by including social factors in the inference models, PCIM achieves 35% of the target customers within 5% of the total number of customers while GICIM is able to reach 85% of the target customers. Both PCIM and GICIM outperform random guessing and models that do not take social factors into account.




Usage metrics


    Ref. manager