Blockbuster Culture\u27s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

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, modeling 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\u27 preferences

Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer\u27s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the Web hyperspecific to our interests, does it fragment Internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product-mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product-mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations

Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

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, modeling 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. Our main result is 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. 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, we show 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 similar users toward the same products. 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

Recommender Systems and their Effects on Consumers: The Fragmentation Debate

Recommender systems are becoming integral to how consumers discover media. The value that recommenders offer is personalization: in environments with many product choices, recommenders personalize the browsing and consumption experience to each user's taste. Popular applications include product recommendations at e-commerce sites and online newspapers' automated selection of articles to display based on the current reader's interests. This ability to focus more closely on one's taste and filter all else out has spawned criticism that recommenders will fragment consumers. Critics say recommenders cause consumers to have less in common with one another and that the media should do more to increase exposure to a variety of content. Others, however, contend that recommenders do the opposite: they may homogenize users because they share information among those who would otherwise not communicate. These are opposing views, discussed in the literature for over ten years for which there is not yet empirical evidence. We present an empirical study of recommender systems in the music industry. In contrast to concerns that users are becoming more fragmented, we find that in our setting users become more similar to one another in their purchases. This increase in similarity occurs for two reasons, which we term volume and taste effects. The volume effect is that consumers simply purchase more after recommendations, increasing the chance of having more purchases in common. The taste effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations. When we view consumers as a similarity network before versus after recommendations, we find that the network becomes denser and smaller, or characterized by shorter inter-user distances. These findings suggest that for this setting, recommender systems are associated with an increase in commonality among users and that concerns of fragmentation may be misplaced

Recommender systems and market diversity

The last ten years have seen a large increase in the number of products available. Many believe this increased variety will allow consumers to obtain more ideal products for themselves. One difficulty that arises, however, is how consumers will find such niche products among so many choices. Recommender systems are one solution to this problem. These systems use data on purchases, ratings, and product content to identify which items are best suited to each user. Although a large body of work exists on designing recommender systems, we know much less about how they affect the market and society. This thesis begins a line of research in that direction, asking what effects recommenders have on the products sold through them and the consumers who use them. Part one asks how recommenders affect products: do recommenders increase the diversity of products sold? Two anecdotal views exist. A common view is that recommenders help consumers discover new products and thus increase sales diversity. Others believe that recommenders only reinforce the popularity of already popular products. Modeling the consumer-recommender interaction as a stochastic process, we find that some recommender designs can reduce sales diversity. In turn, consumers may be underserved if there exist better product matches outside of the hits. We also discuss design modifications that limit these popularity effects and promote exploration. Part two asks how recommenders affect consumers: do they create fragmentation among users? Recommenders give consumers a powerful means to focus on their interests and filter out all other content. As a result, critics argue that recommenders will reduce commonality and create fragmentation. Others, however, contend the opposite: recommenders may homogenize users because they share information among those who would otherwise not communicate. These are opposing views for which there is not yet empirical evidence. In an empirical study of a large service provider in the music industry, we find that recommenders are associated with an increase in commonality among users, and so concerns of fragmentation may be misplaced. The thesis thus identifies a debate about recommender systems in each part, products and consumers, and in each case, the reconciliation appears to challenge a popular view

The

culture’s next rise or fall