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

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    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 is a first attempt 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 four main results. One, some common recommenders lead to a net reduction in average 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 finding is often surprising to consumers who express that recommendations have helped them discover new products. In line with this, result two shows 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 us toward the same new products. Result three finds that recommenders intensify the effects of chance events on market outcomes. At the product level, recommenders can ‘create hits' out of products with early, high sales due to chance alone. At the market level, in individual sample paths it is possible to observe more diversity, even though on average diversity often decreases. Four, we show how basic design choices affect the outcome. Thus, managers can choose recommender designs that are more consistent with their sales or product assortment strategies.

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    File URL: http://www.netinst.org/Fleder-Hosanagar_07-10.pdf
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    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 07-10.

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    Length: 43 pages
    Date of creation: Sep 2007
    Date of revision: Sep 2007
    Handle: RePEc:net:wpaper:0710

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    Web page: http://www.NETinst.org/

    Related research

    Keywords: recommender systems; collaborative filtering; long tail; path dependence; concentration; diversity;

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    Cited by:
    1. Christopher Klein & Shea Slonaker, 2010. "Chart Turnover and Sales in the Recorded Music Industry: 1990–2005," Review of Industrial Organization, Springer, vol. 36(4), pages 351-372, June.
    2. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.

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