AI in recommendation engines

Abstract

We conducted a survey to understand how recommendation engines effects the shopping behavior of users. The purpose of the survey was to get quantitative insights on how the experience of customers changes with the various suggestions they receive while browsing for products. A questionnaire was designed with focus on areas like perceived accuracy, usefulness, satisfaction, trust on using recommendation engine. The effect of vernacular suggestions was also studied via A/B testing. Hypothesis related to these focus areas were formed and tested using the data collected from the survey. We also conducted a telephonic interview of a Data scientist. The purpose of the primary research was to understand the technology behind how the model for recommendation engines is implemented.