Maximizing Product Adoption in a Social Network

Tuesday, February 28, 2012 - 3:30pm - 3:45pm
Keller 3-180
Smriti Bhagat (Technicolor Inc.)
One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. approaches this as the problem of influence maximization that tacitly assumes that a user who is influenced (or, informed) about a product necessarily adopts it and encourages her friends to do the same. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like the product and hence not encourage her friends to adopt it. Previous works do not account for these phenomena.

We argue that it is important to distinguish product adoption from influence. We propose a model that factors in a user’s experience (or projected experience) with a product. We adapt the classical Linear Threshold (LT) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence. We show that under our model, adoption maximization is NP-hard and the objective function is monotone and submodular, thus admitting an approximation algorithm. Our empirical results on three datasets show that our model is able to distinguish between influence and adoption, and predict product adoption much more accurately than the classical LT model.

This is joint work with Amit Goyal and Laks V. S. Lakshmanan.