Next Article in Journal
Dataset for Scheduling Strategies for Microgrids Coupled with Natural Gas Networks
Next Article in Special Issue
Point of Sale (POS) Data from a Supermarket: Transactions and Cashier Operations
Previous Article in Journal
Biogenic Volatiles Emitted from Four Cold-Hardy Grape Cultivars During Ripening
Previous Article in Special Issue
Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle

Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

1
Boxx.ai | AI for E-commerce, Data Science dept., Bengaluru 560095, India
2
University of Michigan, Department of Mathematics, Ann Arbor, USA 48109 & Boxx.ai | AI for E-commerce, Data Science dept., Bengaluru 560095, India
*
Authors to whom correspondence should be addressed.
Received: 10 December 2018 / Revised: 18 January 2019 / Accepted: 28 January 2019 / Published: 31 January 2019
(This article belongs to the Special Issue Data Analysis for Financial Markets)
  |  
PDF [3472 KB, uploaded 1 February 2019]
  |     |  

Abstract

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface—will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics, such as Click-through rates, Buy-through rates, and Conversion revenue. View Full-Text
Keywords: data preprocessing; data validation; recommendation engine; E-commerce; Click-through rate; Buy-through rate; online customer behavior; non-parametric outlier removal; personalization data preprocessing; data validation; recommendation engine; E-commerce; Click-through rate; Buy-through rate; online customer behavior; non-parametric outlier removal; personalization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Chaudhary, N.; Roy Chowdhury, D. Data Preprocessing for Evaluation of Recommendation Models in E-Commerce. Data 2019, 4, 23.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top