Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

Namrata Chaudhary and Drimik Roy Chowdhury
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Namrata Chaudhary: Boxx.ai | AI for E-commerce, Data Science dept., Bengaluru 560095, India
Drimik Roy Chowdhury: University of Michigan, Department of Mathematics, Ann Arbor, USA 48109 & Boxx.ai | AI for E-commerce, Data Science dept., Bengaluru 560095, India

Data, 2019, vol. 4, issue 1, 1-22

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.

Keywords: data preprocessing; data validation; recommendation engine; E-commerce; Click-through rate; Buy-through rate; online customer behavior; non-parametric outlier removal; personalization (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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