KNOWLEDGE DISCOVERY FROM BELIEF NETWORKS

摘要

Techniques are disclosed herein for making predictions with respect to how content consumers will interact with a digital asset. For example, in the context of website visitors browsing digital assets provided via a website, web traffic data can be collected and modeled using a belief network. The belief network may represent a probability distribution for a set of variables that define the web traffic data. Examples of such variables include browser type, browsing session duration, geographic location, visitor demographic characteristics, and a browsing outcome. Certain of the embodiments disclosed herein can be used to extract knowledge from the belief network, thereby allowing statistical inferences to be drawn with respect to how certain classes of website visitors will interact with the website. The influence of one or more first variables (for example, geographic location) can be quantified with respect to one or more second variables (for example, the successful result indicator).

主权项

1. A method for deriving information from a belief network that is used to model web traffic data, the method comprising:
providing a user interface configured to receive a selection of a given variable that is observed to take one or more given values in collected web traffic data, wherein the web traffic data is modeled using a belief network; extracting a plurality of variable-value pairs from the belief network, each extracted variable-value pair comprising an influential variable and a corresponding influential value, wherein each extracted variable-value pair has a statistical influence on a probability distribution of values taken by the given variable; and displaying a ranked list of the plurality of extracted variable-value pairs, the ranked list being ordered based on an influence parameter that quantifies the statistical influence a particular extracted variable-value pair has on the probability distribution of values taken by the given variable.