Influence maximization with viral product design

摘要

The disclosure includes use of a feature-aware propagation model to identify one or more features of a product and one or more person(s), or members of a social network, to target, or user, for marketing the product having the identified features. The one or more person(s) identified using the model may be the person(s), or member(s), of a social network determined to have a maximum capability, relative to other members of the social network, for influencing the members of the social network in adopting, e.g., purchasing, a product having the identified features. In addition, parameters of the model may be determined using information about the social network, user preferences, and the products and features of the products.

主权项

1. A method comprising:
using, by at least one computing system, a feature-aware propagation model having a set of model parameters in selecting a seed set of users that are members of a social network for influencing other users of the social network in combination with selecting, by the feature-aware propagation model, a product feature set comprising a number of features of a product, the feature-aware propagation model further determining a likelihood of the combination to maximize adoption of the product having the product's product feature set by members of the social network; and identifying, by the at least one computing system, the combination of the user seed set of users and the product feature set for the product selected using the feature-aware propagation model, the user seed set comprising a number of users for influencing other users with respect to the product with its product feature set, the identified combination of the seed set of users that are members of a social network for influencing other users of the social network and the product's product feature set having the determined likelihood to maximize adoption of the product having the product's product feature set by members of the social network that is greater than the determined likelihood corresponding to at least one other combination of user seed set and product feature set considered by the feature-aware propagation model.