Kartik Hosanagar

Online product ratings are widely available on the Internet and are known to influence prospective buyers. An emerging literature has started to look at how ratings are generated and in particular how they are influenced by prior ratings. We study the social influence of prior ratings and, in particular, investigate any differential impact of prior ratings by strangers (“crowd”) versus friends. We find evidence of both herding and differentiation behavior in crowd ratings wherein users’ ratings… Show more

Online product ratings are widely available on the Internet and are known to influence prospective buyers. An emerging literature has started to look at how ratings are generated and in particular how they are influenced by prior ratings. We study the social influence of prior ratings and, in particular, investigate any differential impact of prior ratings by strangers (“crowd”) versus friends. We find evidence of both herding and differentiation behavior in crowd ratings wherein users’ ratings are influenced positively or negatively by prior ratings depending on movie popularity. In contrast, friends’ ratings always induce herding. Further, the presence of social networking reduces the likelihood of herding on prior ratings by the crowd. Finally, we find that an increase in the number of friends who can potentially observe a user’s rating (“audience size”) has a positive impact on ratings. These findings raise questions about the reliability of ratings as unbiased indicators of quality and advocate the need for techniques to de- bias rating systems. Show less

In this study, we develop data-driven algorithms for bidding in sponsored search auctions

This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. We find that some well known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and… Show more

This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. We find that some well known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice-versa for unpopular ones. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, result two shows that it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push users toward the same products.. Third, we show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers’ preferences Show less