A provable algorithmic approach to product selection problems for market entry and sustainability

Given the globalized economy, how to process the heterogeneous web data so to extract customers' purchase behavior is crucial to manufacturers who want to enter or sustain in a competitive market. To maximize the sales, manufacturers not only need to decide what products to produce so to meet diverse customers' requirements, but at the same time, compete with competitors' products. In this paper, we present a general framework for the following product selection problems: (1) k-BSP problem, which is for a manufacturer to enter a competitive market, and (2) k-BBP problem, which is for a manufacturer to sustain in a competitive market. We propose several product adoption models to describe the complex purchase behavior of customers, and formally show that these problems are NP-hard in general. To tackle these problems, we propose computationally efficient greedy-based approximation algorithms. Based on the submodularity analysis, we prove that our algorithms can guarantee a (1--1/e)-approximation ratio as compared to the optimal solutions. We perform large scale data analysis to show the efficiency and accuracy of our framework. In our experiments, we observe 1,300 to 250,000 times speedup as compared to the exhaustive algorithms, and our solutions can achieve on average 96% of solution quality as compared to the optimal solutions. Finally, we apply our algorithms on web dataset to show the impact of customers' different purchase behavior on the results of product selection.