Anyone who shops regularly online has encountered recommender systems that point out one or two other products or pieces of content we might like, based on past purchases or other behavior. In two new papers, Wharton operations, information and decisions professor Kartik Hosanagar examined when recommender systems work well, and when they don’t, and whether certain types of products tend to do particularly well when included in such systems. He also looked at how recommender systems interact with other features designed to drive purchases, such as customer reviews. More: http://knlg.net/1NwTohV
Both “People Who Liked This Study Also Liked”: An Empirical Investigation of the Impact of Recommender Systems on Sales Volume and Diversity,” and “When Do Recommender Systems Work The Best? The Moderating Effects Of Product Attributes And Consumer Reviews On Recommender Performance,” were co-authored by Hosanagar and Carnegie Mellon business analytics professor Dokyun Lee.
Hosanagar recently sat down with Knowledge@Wharton to discuss what his research reveals about when we’re most likely to be influenced by those clever algorithms and how these systems are changing the way we discover new products.