Spreading the Oprah Effect: The Diffusion of Demand Shocks in a Recommendation Network

We study the magnitude and persistence of the diffusion of exogenous demand shocks on an ecommerce recommendation network. The demand shocks are generated by book reviews on the Oprah Winfrey Show and in the NYTimes, and the recommendation network is generated by Amazon’s copurchase network. We find a strikingly high level of diffusion of exogenous shock through such networks. Neighboring books experience a dramatic increase in their demand levels, even though they are not actually featured on the review. An average of 40% of neighbors, even 4 clicks away see a statistically significant increase in their demand levels; this effect is indicative of the depth of contagion in online recommendation networks following exogenous shocks. We also document how clustered networks “trap” a higher fraction of the contagion closer to the reviewed book, and we provide summaries of the persistence and relative magnitude of the demand inflation of the neighborhood

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

Is Oprah Contagious? Identifying Demand Spillovers in Product Networks

We study the online contagion of exogenous demand shocks generated by book reviews featured on the Oprah Winfrey TV show and published in the New York Times, through the co-purchase recommendation network on Amazon.com. These exogenous events may ripple through and affect the demand for a 'network' of related books that were not explicitly mentioned in a review but were located 'close' to reviewed books in this network. Using a difference-in-differences matched-sample approach, we identify the extent of the variations caused by the visibility of the online network and distinguish this effect from variation caused by hidden product complementarities. Our results show that the demand shock diffuses to books that are upto five links away from the reviewed book, and that this diffused shock persists for a substantial number of days, although the depth and the magnitude of diffusion varies widely across books at the same network distance from the focal product. We then analyze how product characteristics, assortative mixing and local network structure, play a role in explaining this variation in the depth and persistence of the contagion. Specifically, more clustered local networks 'trap' the diffused demand shocks and cause it to be more intense and of a greater duration but restrict the distance of its spread, while less clustered networks lead to wider contagion of a lower magnitude and duration. Our results provide new evidence of the interplay between a firm's online and offline media strategies and we contribute methods for modeling and analyzing contagion in networks

On The Mobile Behavior of Solid 4^4He at High Temperatures

We report studies of solid helium contained inside a torsional oscillator, at temperatures between 1.07K and 1.87K. We grew single crystals inside the oscillator using commercially pure $^4$He and $^3$He-$^4$He mixtures containing 100 ppm $^3$He. Crystals were grown at constant temperature and pressure on the melting curve. At the end of the growth, the crystals were disordered, following which they partially decoupled from the oscillator. The fraction of the decoupled He mass was temperature and velocity dependent. Around 1K, the decoupled mass fraction for crystals grown from the mixture reached a limiting value of around 35%. In the case of crystals grown using commercially pure $^4$He at temperatures below 1.3K, this fraction was much smaller. This difference could possibly be associated with the roughening transition at the solid-liquid interface.Comment: 15 pages, 6 figure

Is Oprah Contagious? Identifying Demand Spillovers in Product Networks

We study the online contagion of exogenous demand shocks generated by book reviews featured on the Oprah Winfrey TV show and published in the New York Times, through the co-purchase recommendation network on Amazon.com. These exogenous events may ripple through and affect the demand for a “network” of related books that were not explicitly mentioned in a review but were located “close” to reviewed books in this network. Using a difference-in-differences matched-sample approach, we identify the extent of the variations caused by the visibility of the online network and distinguish this effect from variation caused by hidden product complementarities. Our results show that the demand shock diffuses to books that are up to five links away from the reviewed book, and that this diffused shock persists for a substantial number of days, although the depth and the magnitude of diffusion varies widely across books at the same network distance from the focal product. We then analyze how product characteristics, assortative mixing and local network structure, play a role in explaining this variation in the depth and persistence of the contagion. Specifically, more clustered local networks “trap” the diffused demand shocks and cause it to be more intense and of a greater duration but restrict the distance of its spread, while less clustered networks lead to wider contagion of a lower magnitude and duration. Our results provide new evidence of the interplay between a firm’s online and offline media strategies and we contribute methods for modeling and analyzing contagion in networks.networks, product networks, electronic commerce, ecommerce, recommender systems, identification, exogenous shocks

Is Oprah Contagious? The Depth of Diffusion of Demand Shocks in a Product Network

Recent studies have documented that the contagion of information and behaviors in social networks is generally quite limited. We examine whether this pattern characterizes exogenous demand shocks diffusing in a product network. To this end, we analyze a unique series of demand shocks induced by mass-media book reviews on the Oprah Winfrey television show and in The New York Times. Our identification strategy is based on a difference-in-differences model estimated using two different groups as control, based on propensity-score-based matching and network proximity to a reviewed book, respectively. Our results show that the diffusion of exogenous demand shocks in the Amazon.com product network is relatively shallow, typically about three edges deep into the network, although the economic impact of this diffusion can often be significant. We link our results to recent findings in the context of diffusion in social networks and discuss managerial implications

Resolution enhancement in MRI

We consider the problem of super-resolution reconstruction (SRR) in MRI. Subpixel-shifted MR images were taken in several fields of view (FOVs) to reconstruct a high-resolution image. A novel algorithm is presented. The algorithm can be applied locally and guarantees perfect reconstruction in the absence of noise. Results that demonstrate resolution improvement are given for phantom studies (mathematical model) as well as for MRI studies of a phantom carried out with a GE clinical scanner. The method raises questions that are discussed in the last section of the paper. Open questions should be answered in order to apply this method for clinical purposes

Assessing Value in an Online Network of Products

Traditionally, the value of a product is assessed according to its direct revenues. However, products do not exist in isolation but rather influence one another\u27s sales. Such influence is especially evident in eCommerce environments, where products are presented as a large-scale product network. We present the first attempt to use a systematic approach to estimate products\u27 true value to a firm in such settings. We separate a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. Using data from the network of books on Amazon, we examine the relationship between revenue and the sources of value. We show that the value of low-sellers is underestimated when focusing on direct revenue, while the value of bestsellers is overestimated. We explore the sources of this discrepancy and discuss the implications for managing products in an environment of product networks