Abstract: Embodiments disclosed provide technical details on fractional attribution using online content provision information. More specifically, embodiments disclosed herein use historical data to determine one or more conditional probabilities and assign credit weights to given events. In this way, more accurate attribution of conversions to particular events may be assigned.

Abstract: Embodiments disclosed provide new approaches for determining fractional attribution using aggregate advertising information. A channel weighting approach may derive the causal influence weight of any channel on conversions. In some embodiments, the approach may include arranging the conversion rate of each channel into different funnel stages, constructing aggregate-level data, and running a multi-stage regression computation using instrumental variables. This approach works with any number of different types of advertising channels, including online and offline channels, and provides the most accurate credit to each channel or sub-channel involved.

Abstract: Embodiments provide fractional attribution using aggregate-level information as well as user-level data. For example, aggregate data may be used to determine marginal conversion probabilities for individual attributes within each channel. For channels that have user-level data, the marginal conversion probabilities may be determined using user-level data associated with converted users and aggregate-level data associated with non-converting users. Different channels may have different attributes and the channels may be weighted, in one embodiment, via a causal analysis using instrumental variables. Each conversion path may be characterized by a set of attributes. Additionally, each conversion path may have touch points. The marginal conversion probabilities for the attributes may be combined to produce an importance weight for each touch point on a converting path. These importance weights can be normalized across the touch points on the converting path to obtain attribution results.

Abstract: Embodiments disclosed provide technical details on fractional attribution using online advertising information. More specifically, embodiments disclosed herein use historical data to determine one or more conditional probabilities and assign credit weights to given events. In this way, fairer and more accurate attribution of conversions to particular events may be assigned.

Abstract: Embodiments disclosed herein seamlessly integrate several components into a comprehensive collusion detection and traffic quality prediction system, including a strong modeling module for processing historical click data and transforming potential collusions hidden therein into solvable graph partitioning (network) and/or vector space clustering (pattern) models, a scalable and robust toolkit comprising a plurality of graph partitioning and clustering heuristics for analyzing and generating high density subgraphs and high dimensional clusters or groups, and a post processing module for extracting entities from the subgraphs and clusters and placing them on global block lists. Entities thus listed can be blocked from client networks in real time. As such, high traffic quality can be predicted. A job scheduler may schedule individual jobs from the modeling module based on the number of available resources in a distributed computing environment to minimize completion time while balancing load.

Abstract: Embodiments disclosed herein provide a practical solution for click fraud detection. One embodiment of a method may comprise constructing representations of entities via a graph network framework. The representations, graphs or vector spaces, may capture information pertaining to clicks by botnets/click farms. To detect click fraud, each representation may be analyzed in the context of clustering, resulting in large data sets with respect to time, frequency, or gap between clicks. Highly accurate and highly scalable heuristics may be developed/applied to identify IP addresses that indicate potential collusion.