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

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

What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects

Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business-oriented metrics such as click-through-rates, customer retention or effects on the sales spectrum might be the true evaluation criteria for RS effec-tiveness. In this paper, we compare different RS algorithms with respect to their tendency of focusing on certain parts of the product spectrum. Our first analysis on different data sets shows that some algorithms – while able to generate highly accurate predictions – concentrate their top 10 recommendations on a very small fraction of the product cata-log or have a strong bias to recommending only relatively popular items than others. We see our work as a further step toward multiple-metric offline evaluation and to help service providers make better-informed de-cisions when looking for a recommendation strategy that is in line with the overall goals of the recommendation service.

Effects of vibrations and shocks in electric vehicles on Li-ion batteries

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