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.
There are numerous other models of influence spread in social networks. One of the first and most influential diffusion models was proposed by Bass @cite_5 . The model of product diffusion predicts the number of people who will adopt an innovation over time. It does not explicitly account for the structure of the social network but it rather assumes that the rate of adoption is a function of the current proportion of the population who have already adopted (purchased a product in our case). The diffusion equation models the cumulative proportion of adopters in the population as a function of the intrinsic adoption rate, and a measure of social contagion. The model describes an S-shaped curve, where adoption is slow at first, takes off exponentially and flattens at the end. It can effectively model word-of-mouth product diffusion at the aggregate level, but not at the level of an individual person, which is one of the topics we explore in this paper.
@cite_5
2206697650
The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use.