Impact of Recommender Systems on Sales Diversity

1. Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity By: Daneil Fleder and Kartik Hosanagar Discussed by: Mo Xiao, U. of Arizona NET Institute Conference on Network Economics April 2008

2. The Big Picture • Why would a firm adopt a recommender system? • Creating switching costs (and thus barriers to entry)? • Increasing site traffic? • Selling more? • Introducing new products? • Benefiting from a more concentrated sale? Say, easier inventory decision? • The actual (and sometimes unintended) effects?

3. This Paper • Focuses on positive side of question, in particular, the impact of such a system on sales diversity • The challenges to model individual consumer choice with a recommender system: • Evolving consumers’ choice set, which is endogeneously determined by the system • Need to model how consumers process information • Recommender does not just follow one rule

4. Main Features of the Model • Consumers’ choice set fixed • Only 2 products in the baseline model • Limited consumer choice • Consumer accepts recommendation with a fixed probability in the baseline model • Salience factor in simulation • Recommends only depend on market shares

5. Results • Recommender system increases individual diversity but reduces aggregate diversity • Initial conditions (or chance) matters a great deal. The recommender reinforces the popularity of the “initially” preferred product

6. Main Complaint • Model does NOT allow entry of new products. • What is the purpose of a recommender system if a consumer’s choice set is fixed? • More intriguing question: Markets will become de-concentrated with entry of new products. Can recommender system reverse the trend? • To model entry of new products, the authors need to at least: • Change the rule by which a product is recommended. Can not be just depending on market shares • Model how consumers take a recommended product into utility function (the caveat of the salience factor)

7. Interpretation on Results • By authors: increased aggregated concentration reinforces the blockbuster nature of media • Question: is “blockbuster nature” good or bad? • Paper assesses product fit for consumers, but no consumer surplus analysis • Without formulating and solving a firm’s optimization problem, paper can not analyze producer welfare • Again, the boost in sales is due to salience factor increasing mean utility level of product

8. Suggestions • Motivate the paper with stats: • Has aggregate concentration increased over time? • Before and after a recommender system was put in place? (say use NETFLIX’s data to get some stylized facts) • Incorporate “awareness” role of the system in the baseline model • The recommender system changes consumers’ choice through changing choice set, not through a fixed probability

9. Minor Issues • Notation: i indexes for both customers and recommendation design choice, j indexes for both products and, again, recommendation design choice. • Need to give the intuition why sales concentration may occasionally decrease • Table 1, 6th column: why does AUIBP decrease with the system while AUIAP increases?