Does Utilizing Online Social Relations Improve the Diversity of Personalized Recommendations?

Abstract

Personalized recommendations are widely used to improve customer experience and drive sales. Traditional recommender systems typically focus on using accuracy as the key metric to evaluate the performance of personalized recommendations. However, recent studies suggest that recommending a diverse list of products improves user satisfaction and is positively associated with customer retention rates. In this study, we propose to incorporate the product ratings from users’ online social relations into recommendation model to enhance the diversity of personalized recommendation list. The empirical results indicate that our proposed approach performs well in increasing the recommendation diversity while maintaining comparable level of accuracy. The findings offer practical implications for online businesses to leverage online social relations.