Book•
The Long Tail: Why the Future of Business is Selling Less of More
11 Jul 2006-
TL;DR: The rise of the long tail is a powerful new force i n our economy as discussed by the authors, which is not just a virtue of online marketplaces; it is an example of an entirely new economic model for business, one that is just beginning to show its power.
Abstract: What happens when the bottlenecks that stand between supply and demand in our culture go away and everything becomes available to everyone? The Long Tail is a powerful new force i n our economy: the rise of the niche. As the cost of reaching consumers drops dramatically, our markets are shifting from a one-size-fits-all model of mass appeal to one of unlimited variety for unique tastes. From supermarket shelves to advertising agencies, the ability to offer vast choice is changing everything, and causing us to rethink where our markets lie and how to get to them. Unlimited selection is revealing truths about what consumers want and how they want to get it, from DVDs at Netflix to songs on iTunes to advertising on Google. However, this is not just a virtue of online marketplaces; it is an example of an entirely new economic model for business, one that is just beginning to show its power. After a century of obsessing over the few products at the head of the demand curve, the new economics of distribution allow us to turn our focus to the many more products in the tail, which collectively can create a new market as big as the one we already know. The Long Tail is really about the economics of abundance. New efficiencies in distribution, manufacturing, and marketing are essentially resetting the definition of what’s commercially viable across the board. If the 20th century was about hits, the 21st will be equally about niches.
Citations
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TL;DR: While on average recommendations are not very effective at inducing purchases and do not spread very far, this work presents a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.
Abstract: We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.
2,361 citations
Cites background from "The Long Tail: Why the Future of Bu..."
...Recently there has been some debate about the long tail [Gomes 2006; Anderson 2006]....
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18 May 2008
TL;DR: This work applies the de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service, and demonstrates that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset.
Abstract: We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information
2,241 citations
Cites background from "The Long Tail: Why the Future of Bu..."
...The distribution of per-attribute support sizes is typically heavy- or long-tailed, roughly following the power law [7, 4]....
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...This sparsity is empirically well-established [7, 4, 19] and related to the “fat tail” phenomenon: individual transaction and preference records tend to include statistically rare attributes....
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TL;DR: This article examined how product and consumer characteristics moderate the influence of online consumer reviews on product sales using data from the video game industry and found that online reviews are more influential for less popular games and games whose players have greater Internet experience.
Abstract: This article examines how product and consumer characteristics moderate the influence of online consumer reviews on product sales using data from the video game industry. The findings indicate that online reviews are more influential for less popular games and games whose players have greater Internet experience. The article shows differential impact of consumer reviews across products in the same product category and suggests that firms' online marketing strategies should be contingent on product and consumer characteristics. The authors discuss the implications of these results in light of the increased share of niche products in recent years.
1,952 citations
Book•
01 Oct 2011TL;DR: This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets, and explains the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing.
Abstract: The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.
1,795 citations
24 Oct 2007
TL;DR: In this article, the authors analyzed YouTube, the world's largest UGC VoD system, and provided an in-depth study of the popularity life cycle of videos, intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content.
Abstract: User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications.
1,713 citations