Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
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In this paper, the authors examine the effect of recommender systems on the diversity of sales and show that it is possible for individual-level diversity to increase but aggregate diversity to decrease.Abstract:
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' preferences.read more
Citations
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The Impact of New Media on Customer Relationships
Thorsten Hennig-Thurau,Thorsten Hennig-Thurau,Edward C. Malthouse,Christian Friege,Sonja Gensler,Lara Lobschat,Arvind Rangaswamy,Bernd Skiera +7 more
TL;DR: In this article, the authors introduce a new "pinball" framework of new media's impact on relationships with customers and identify key new media phenomena which companies should take into account when managing their relationships with customer in the new media universe.
Journal ArticleDOI
Recommender systems: from algorithms to user experience
Joseph A. Konstan,John Riedl +1 more
TL;DR: It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested.
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Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
TL;DR: A number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy are introduced and explored.
Proceedings ArticleDOI
Rank and relevance in novelty and diversity metrics for recommender systems
Saúl Vargas,Pablo Castells +1 more
TL;DR: A formal framework for the definition of novelty and diversity metrics is presented that unifies and generalizes several state of the art metrics and identifies three essential ground concepts at the roots of noveltyand diversity: choice, discovery and relevance, upon which the framework is built.
Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales
TL;DR: Krishnan et al. as mentioned in this paper investigated the long tail phenomenon of the Pareto principle and found that consumers' usage of Internet search and discovery tools, such as recommendation engines, is associated with an increase in the share of niche products.
References
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Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.