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.
In the context of the internet word-of-mouth advertising is not restricted to pairwise or small-group interactions between individuals. Rather, customers can share their experiences and opinions regarding a product with everyone. Quantitative marketing techniques have been proposed @cite_17 to describe product information flow online, and the rating of products and merchants has been shown to effect the likelihood of an item being bought @cite_23 @cite_27 . More sophisticated online recommendation systems allow users to rate others' reviews, or directly rate other reviewers to implicitly form a trusted reviewer network that may have very little overlap with a person's actual social circle. Richardson and Domingos @cite_37 used Epinions' trusted reviewer network to construct an algorithm to maximize viral marketing efficiency assuming that individuals' probability of purchasing a product depends on the opinions on the trusted peers in their network. Kempe, Kleinberg and Tardos @cite_33 have followed up on Richardson and Domingos' challenge of maximizing viral information spread by evaluating several algorithms given various models of adoption we discuss next.
@cite_37 @cite_33 @cite_27 @cite_23 @cite_17
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We study the use of viral marketing strategies on social networks to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product and the price at which the product is offered. The influence model we analyze is quite general, naturally extending both the Linear Threshold model and the Independent Cascade model, while also incorporating price information. We consider sales proceeding in a cascading manner through the network, i.e. a buyer is offered the product via recommendations from its neighbors who own the product. In this setting, the seller influences events by offering a cashback to recommenders and by setting prices (via coupons or discounts) for each buyer in the social network. Finding a seller strategy which maximizes the expected revenue in this setting turns out to be NP-hard. However, we propose a seller strategy that generates revenue guaranteed to be within a constant factor of the optimal strategy in a wide variety of models. The strategy is based on an influence-and-exploit idea, and it consists of finding the right trade-off at each time step between: generating revenue from the current user versus offering the product for free and using the influence generated from this sale later in the process. We also show how local search can be used to improve the performance of this technique in practice. Online reviews have become an inevitable part of a consumer’s decision making process, where the likelihood of purchase not only depends on the product’s overall rating, but also on the description of it’s aspects. Therefore, websites such as Amazon, Walmart, and Netflix constantly encourage users to write good quality reviews and categorically summa- rize different facets of the product. However, despite such efforts, it takes a significant effort to skim through thousands of reviews and look for answers that addresses the query of consumers. For example, a gamer might be interested in buying a monitor with fast refresh rates, support for Gsync and Freesync technologies etc., while a photographer might be interested in aspects such as color reproduction and Delta-e scores. Therefore, in this paper, we propose a generative aspect summarization model called APSUM that is capable of providing fine-grained summaries of on- line reviews. To overcome the inherent problem of aspect sparsity, we jointly constraint both the document-topic and the word-topic distribution by introducing a semi-supervised variation of the spike-and-slab prior. Using rigorous set of experiments, we show that the proposed model is capable of outperforming other state-of-the-art aspect-topic models over a variety of datasets and deliver intuitive fine-grained summaries that could simplify the purchase decisions of customers. The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times. Recommender systems predict user preferences based on a range of available information. For systems in which users generate streams of content (e.g., blogs, periodically-updated newsfeeds), users may rate the produced content that they read, and be given accurate predictions about future content they are most likely to prefer. We design a distributed mechanism for predicting user ratings that avoids the disclosure of information to a centralized authority or an untrusted third party: users disclose the rating they give to certain content only to the user that produced this content. We demonstrate how rating prediction in this context can be formulated as a matrix factorization problem. Using this intuition, we propose a distributed gradient descent algorithm for its solution that abides with the above restriction on how information is exchanged between users. We formally analyse the convergence properties of this algorithm, showing that it reduces a weighted root mean square error of the accuracy of predictions. Although our algorithm may be used many different ways, we evaluate it on the Neflix data set and prediction problem as a benchmark. In addition to the improved privacy properties that stem from its distributed nature, our algorithm is competitive with current centralized solutions. Finally, we demonstrate the algorithm's fast convergence in practice by conducting an online experiment with a prototype user-generated content exchange system implemented as a Facebook application.