MANAGEMIENT SCIENCE ToPms vol.55No.5,May2009,pp.697-712 Do110.1287/mnsc.10800974 IssN0025-1909|EssN1526-55011091550510697 @2009 INFORMS Blockbuster Culture's next rise or fall The Impact of Recommender Systems on Sales diversity Daniel Fleder, Kartik Hosanagar and Informatio delphia, Pennsylvania 19104 Idfleder@wharton. upenn. edu, kartik@wharton. up his 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. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, mod- eling recommender systems analytically allows us to explore their path-dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. We arrive at three main results. First, 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. This bias toward popularity can prevent what may otherwise be better consumer- product matches. That diversity can decrease is surprising to consumers who express that recommendations ave 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 Key words: If policy and management; electronic commerce; application contexts/sectors; IT impacts on industry and market structure; marketing, advertising and media History: Received June 21, 2007; accepted November 18, 2008, by Barrie Nault, information systems. Published online in Articles in Advance March 6, 2009 1. Introduction filters, [which include online recommender systems media has historically been a"blockbuster"industry is to help people move from the world they know (Anderson 2006). Of the many products available, (hits) to the world they don' t (niches")"(Anderson sales have concentrated among a small number of 2006, p. 109) hits. In recent years, such concentration has begun Although recommenders have been assumed to to decrease. The last 10 years have seen an extraor- push consumers toward the niches, we present an linary increase in the number of products available argument why some popular systems might do the (Brynjolfsson et al. 2006, Clemons et al. 2006),and opposite. Anecdotes from users and researchers sug- consumers have taken to these expanded offerings. gest that recommenders help consumers discover ne Many believe this increased variety allows consumers products and thus increase diversity(Anderson 2006) to obtain more ideal products, and if it continues it Others believe several recommender designs might could amount to a cultural shift from hit to niche reinforce the position of already-popular products products. One difficulty that arises, however, is how and thus reduce diversity(Mooney and Roy 2000, consumers will find such niche products among seem- Fleder and Hosanagar 2007). This paper attempts to ingly endless alternatives Recommender systems are considered one solu ing supply-side offerings fixed, we ask whether rec tion to this problem. These systems use data on pur- ommenders make media consumption more diverse chases, product ratings, and user profiles to predict or more concentrated which products are best suited to a particular user. 1 with ate a univer These systems are commonplace at major online firms different recommenders employed cannot st al result for all. Instead, this paper picks sev- such as Amazon, Netflix, and Apple's iTunes Store. eral recommenders that we believe are commonly used in industry In author Chris Andersons view " The main effect of and focuses on them.MANAGEMENT SCIENCE Vol. 55, No. 5, May 2009, pp. 697–712 issn0025-1909 eissn1526-5501 09 5505 0697 informs ® doi 10.1287/mnsc.1080.0974 © 2009 INFORMS Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity Daniel Fleder, Kartik Hosanagar Department of Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104 {dfleder@wharton.upenn.edu, kartikh@wharton.upenn.edu} 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. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, mod￾eling recommender systems analytically allows us to explore their path-dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. We arrive at three main results. First, 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. This bias toward popularity can prevent what may otherwise be better consumer￾product matches. 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. Key words: IT policy and management; electronic commerce; application contexts/sectors; IT impacts on industry and market structure; marketing; advertising and media History: Received June 21, 2007; accepted November 18, 2008, by Barrie Nault, information systems. Published online in Articles in Advance March 6, 2009. 1. Introduction Media has historically been a “blockbuster” industry (Anderson 2006). Of the many products available, sales have concentrated among a small number of hits. In recent years, such concentration has begun to decrease. The last 10 years have seen an extraor￾dinary increase in the number of products available (Brynjolfsson et al. 2006, Clemons et al. 2006), and consumers have taken to these expanded offerings. Many believe this increased variety allows consumers to obtain more ideal products, and if it continues it could amount to a cultural shift from hit to niche products. One difficulty that arises, however, is how consumers will find such niche products among seem￾ingly endless alternatives. Recommender systems are considered one solu￾tion to this problem. These systems use data on pur￾chases, product ratings, and user profiles to predict which products are best suited to a particular user. These systems are commonplace at major online firms such as Amazon, Netflix, and Apple’s iTunes Store. In author Chris Anderson’s view, “The main effect of filters, [which include online recommender systems], is to help people move from the world they know (‘hits’) to the world they don’t (‘niches’)” (Anderson 2006, p. 109). Although recommenders have been assumed to push consumers toward the niches, we present an argument why some popular systems might do the opposite.1 Anecdotes from users and researchers sug￾gest that recommenders help consumers discover new products and thus increase diversity (Anderson 2006). Others believe several recommender designs might reinforce the position of already-popular products and thus reduce diversity (Mooney and Roy 2000, Fleder and Hosanagar 2007). This paper attempts to reconcile these seemingly incompatible views. Hold￾ing supply-side offerings fixed, we ask whether rec￾ommenders make media consumption more diverse or more concentrated. 1 With so many different recommenders employed by firms, one cannot state a universal result for all. Instead, this paper picks sev￾eral recommenders that we believe are commonly used in industry and focuses on them. 697