Investigating the Role of User Experience and Design in Recommender Systems: A Pragmatic Review

Abstract

The world is growing smarter with technology-driven applications in assisting lifestyle of humans. Machine learning has predominantly helped businesses in continuously learning and evaluating human likes and dislikes. Today, deep neural networks have grabbed the attention of many researchers in developing highly complicated yet effective models for prediction and data analytics. Recommender systems being one of the applications of machine learning has caught customer attention especially in the retail and hospitality sector. After a recommendation engine is built, it is evaluated in different tangents like accuracy, recall, etc. However, there is a serious need to shift the research focus to the user experience and design aspect while using such recommender systems. This paper conducts a survey and based on the survey results, a novel conceptualized model labelled 'Cognitive Dynamic Design Engine' (CDDE) is proposed. This model will help businesses to understand customer mindset, perception and usability parameters while presenting recommendations to their customers. This model is proposed to run on top the deep neural network model that is constructed for generating recommendations. This survey is collected from masses of urban and rural parts of a geographical zone. The results of the study show that user experience and aesthetics of the recommendation given to a user inevitably have a direct effect on users buying decisions.