Preprint Article Version 2 This version is not peer-reviewed

Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

Version 1 : Received: 28 January 2019 / Approved: 29 January 2019 / Online: 29 January 2019 (09:49:55 CET)
Version 2 : Received: 1 February 2019 / Approved: 1 February 2019 / Online: 1 February 2019 (10:22:37 CET)

How to cite: Chaudhary, N.; Roy Chowdhury, D. Data Preprocessing for Evaluation of Recommendation Models in E-Commerce . Preprints 2019, 2019010294 (doi: 10.20944/preprints201901.0294.v2). Chaudhary, N.; Roy Chowdhury, D. Data Preprocessing for Evaluation of Recommendation Models in E-Commerce . Preprints 2019, 2019010294 (doi: 10.20944/preprints201901.0294.v2).

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.

Subject Areas

Data preprocessing; data validation; recommendation engine; E-commerce; Click-through rate; Buy-through rate; online customer behavior; non-parametric outlier removal; personalization

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