Personalized Recommendation System Design for an Online B2B Platform

39 Pages Posted: 12 Aug 2021

See all articles by Vishal Gaur

Vishal Gaur

Cornell University - Samuel Curtis Johnson Graduate School of Management

Xiaoyan Liu

Santa Clara University, Leavey School of Business; Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: November 11, 2020

Abstract

We formulate the problem of designing a personalized recommendation system for an online business-to-business (B2B) marketplace, propose a method to solve it, and evaluate results using a controlled pilot test. Our research is conducted in collaboration with IndiaMart, the dominant online B2B platform in India serving approximately 60 million buyer fi rms and 5 million seller fi rms in more than 50 million products and services. In this problem, buyers place requests for quotation (RFQs) to the platform, and the objective of the platform is to match the RFQs with suitable sellers with the highest likelihood of acceptance. Our problem entails two major challenges: (1) high-dimensional and sparse data regarding product category and spatial engagement, and (2) class imbalance because the volume of "accepted" records in historical clickstream data is significantly larger than that of "declined" records. We propose new variables motivated by the choice estimation literature to address high-dimensionality, and evaluate alternative approaches including the Synthetic Minority Over-sampling Technique (SMOTE) and a new resampling approach, which we call Panel Data Augmentation Technique (PDATE), to counter class imbalance. Our method yields a significant improvement in out-of-sample predictive accuracy. A controlled pilot test conducted at IndiaMart shows that our method provides a consistent and significant improvement in the quality of recommendations sustained over time.

Keywords: Online marketplace, recommendation system, high-dimensionality, class imbalance, field experiment, choice estimation, machine learning

JEL Classification: C51, C52, C55, C93, L81, M11, M21

Suggested Citation

Gaur, Vishal and Liu, Xiaoyan and Liu, Xiaoyan, Personalized Recommendation System Design for an Online B2B Platform (November 11, 2020). Available at SSRN: https://ssrn.com/abstract=3902710 or http://dx.doi.org/10.2139/ssrn.3902710

Vishal Gaur (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.johnson.cornell.edu/faculty/profiles/Gaur/

Xiaoyan Liu

Santa Clara University, Leavey School of Business ( email )

500 El Camino Real
Santa Clara, CA California 95053
United States

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
197
Abstract Views
607
rank
212,098
PlumX Metrics