Recommendation Networks and the Long Tail of Electronic Commerce

307 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Innovation diffusion and new product growth models: A critical review and research directions (2010, methodology): “For online networks, automatic collection methods applied in computer science could be used (Oestreicher-Singer & Sundararajan, 2008, 2009).”
Evaluating Recommender Systems with User Experiments (2015, background): “” [89, 90]) and “smart defaults” (recommendations as default settings for yes/no or multiple-option decisions [61, 105]).”
Marketing in Computer-Mediated Environments: Research Synthesis and New Directions (2014, unknown): “Oestreicher-Singer and Sundararajan (2012) also note the significance of product-related contin-gencies and report that consumer–consumer interactions (specifically, recommendations) are more likely to elevate demand for niche products than for popular, mainstream products.”
Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending (2013, unknown): “Studies draw from networking websites where network ties are explicit (e.g., Aral and Walker 2011, Susarla et al. 2012, Gnyawali et al. 2010) or copurchase and recommendation networks (e.g., Oestreicher-Singer and Sundararajan 2012).”