Universality in movie rating distributions

In this paper histograms of user ratings for movies (1,...,10) are analysed. The evolving stabilised shapes of histograms follow the rule that all are either double- or triple-peaked. Moreover, at most one peak can be on the central bins 2,...,9 and the distribution in these bins looks smooth `Gaussian-like' while changes at the extremes (1 and 10) often look abrupt. It is shown that this is well approximated under the assumption that histograms are confined and discretised probability density functions of L\'evy skew alpha-stable distributions. These distributions are the only stable distributions which could emerge due to a generalized central limit theorem from averaging of various independent random avriables as which one can see the initial opinions of users. Averaging is also an appropriate assumption about the social process which underlies the process of continuous opinion formation. Surprisingly, not the normal distribution achieves the best fit over histograms obseved on the web, but distributions with fat tails which decay as power-laws with exponent -(1+alpha) (alpha=4/3). The scale and skewness parameters of the Levy skew alpha-stable distributions seem to depend on the deviation from an average movie (with mean about 7.6). The histogram of such an average movie has no skewness and is the most narrow one. If a movie deviates from average the distribution gets broader and skew. The skewness pronounces the deviation. This is used to construct a one parameter fit which gives some evidence of universality in processes of continuous opinion dynamics about taste.Comment: 8 pages, 5 figures, accepted for publicatio

An Investigation of Free Product Sampling and Rating Bias in E-Commerce

Free product sampling has increasingly become a popular promotional strategy, and served as a new mechanism of product review generation in e-commerce. We empirically analyze how a product’s engagement in free product sampling affects the product’s review rating, and also examine important contingent factors of product pricing and product popularity. Using a rich data set from Taobao.com and multiple identification strategies and estimation methods, we find that engaging in free product sampling increases product rating by 1.1%. We argue that it is consumers’ reciprocal behavior of giving higher ratings as a return to retailers’ beneficial actions that causes rating bias. We further find that the bias would be larger with higher original price, but smaller with higher price discount and higher product popularity. Our empirical findings provide important contributions to the literature on product sampling and word-of-mouth, and offer critical managerial implications to online retailers, rating system designers, and consumers

Scalable Recommendation with Poisson Factorization

We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchases). In contrast to traditional matrix factorization approaches, Poisson factorization implicitly models each user's limited attention to consume items. Moreover, because of the mathematical form of the Poisson likelihood, the model needs only to explicitly consider the observed entries in the matrix, leading to both scalable computation and good predictive performance. We develop a variational inference algorithm for approximate posterior inference that scales up to massive data sets. This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations. We apply our method to large real-world user data containing users rating movies, users listening to songs, and users reading scientific papers. In all these settings, Bayesian Poisson factorization outperforms state-of-the-art matrix factorization methods

Software Marketing on the Internet: the Use of Samples and Repositories

This paper examines one of the most important marketing strategies by software producers on the Internet. That is whether to offer free samples and if so, whether to list the samples on shareware repositories. I show that firms with higher value products have a greater incentive to offer free samples but are more reluctant to do so if they are well known, and even when they do are less likely to be listed on shareware repositories. I then proceed to use four types of Probit-based models to corroborate the findings from the theoretical model.Shareware; Software; Internet; Distribution; Intermediation; Directory; Repository; Advertising; Brand; Reputation; Asymmetric Information; Search; Sample

Manager's degree of JIT involvement, locus of control and managerial performance

The competitive global environment has lead many firms into adopting practices that focus on eliminating inefficiencies across the enterprise and its supply chain. The Just-in-Time philosophy is one such practice, however, research has predominantly focused on its technical features and on organisational variables, with surprising little research at the individual level. This paper examines JIT at an individual level and argues that the manager’s locus of control orientation would interact with their degree of JIT involvement to affect managerial performance. The results of a survey of 60 managers employing JIT, demonstrate that an increased degree of JIT involvement leads to a more positive effect on managerial performance for internal locus of control managers than for external locus of control managers

The Effects of Online Incentivized Reviews on Organic Review Ratings

As online reviews become a major factor in the consumer decision-making process, firms have started seeking ways to create and leverage reviews to help achieve their marketing objectives. One productive strategy to generate reviews is to incentivize or reward customers to write reviews. While such a strategy certainly augments the number of reviews, it naturally raises questions of how unbiased such reviews are, and how such a bias, if it exists, affects potential customers. Complicating the issue further, such incentives can be provided by either the vendor or the platform, which may affect the nature of bias. To understand the marketing value of such reviews, this research examines the effects of online incentivized reviews on subsequent organic reviews. First, we investigate whether incentivized reviews are biased compared to organic reviews. Specifically, we find that vendor – initiated incentivized reviews are more favorable whereas platform – initiated incentivized reviews are more critical. Second, we study how incentivized reviews affect future organic review ratings. The findings suggest that vendor (platform) – initiated incentivized reviews reduce (increase) the subsequent organic review ratings. Moderating effects of helpfulness of incentivized reviews and product type are significant. These findings offer important insights about the effectiveness of incentivized reviews

Reducing bias and quantifying uncertainty in watershed flux estimates: the R package loadflex

Many ecological insights into the function of rivers and watersheds emerge from quantifying the flux of solutes or suspended materials in rivers. Numerous methods for flux estimation have been described, and each has its strengths and weaknesses. Currently, the largest practical challenges in flux estimation are to select among these methods and to implement or apply whichever method is chosen. To ease this process of method selection and application, we have written an R software package called loadflex that implements several of the most popular methods for flux estimation, including regressions, interpolations, and the special case of interpolation known as the period-weighted approach. Our package also implements a lesser-known and empirically promising approach called the “composite method,” to which we have added an algorithm for estimating prediction uncertainty. Here we describe the structure and key features of loadflex, with a special emphasis on the rationale and details of our composite method implementation. We then demonstrate the use of loadflex by fitting four different models to nitrate data from the Lamprey River in southeastern New Hampshire, where two large floods in 2006–2007 are hypothesized to have driven a long-term shift in nitrate concentrations and fluxes from the watershed. The models each give believable estimates, and yet they yield different answers for whether and how the floods altered nitrate loads. In general, the best modeling approach for each new dataset will depend on the specific site and solute of interest, and researchers need to make an informed choice among the many possible models. Our package addresses this need by making it simple to apply and compare multiple load estimation models, ultimately allowing researchers to estimate riverine concentrations and fluxes with greater ease and accuracy