Dynamic Network Invitation Targeting via Vector Clustering
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Solution Overview
Problem
Existing techniques for determining which non-users are likely to be interested in joining a software application network are inefficient and inaccurate due to limited data availability, reliance on static models, and the need for large amounts of labeled training data, leading to high costs and low accuracy in predicting invitation acceptance.
Innovation Solution
A dynamic targeting method that clusters users into active and passive groups based on engagement levels, generates vector representations of users and non-users from interaction data, and uses an 'explore and exploit' feedback loop to iteratively improve invitation selection without requiring extensive labeled training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If invitations are sent to all known non-users to grow customer base, then network expansion opportunity increases, but computing resource consumption increases and invitation effectiveness decreases
Solution Approach 1:
The patent applies local quality by segmenting the non-user population into distinct clusters based on their network connections and characteristics. Instead of treating all non-users uniformly, the system identifies specific local groups (clusters) that are more likely to accept invitations, thereby improving invitation effectiveness while reducing the total number of invitations needed.
Solution Approach 2:
The patent segments non-users into multiple clusters based on their connection patterns to in-network entities. This segmentation allows the system to target invitations more precisely to specific segments that show higher acceptance potential, rather than sending invitations to all non-users indiscriminately.
2Device complexity
If static models are used to predict non-user interest based on known attributes, then model construction is simpler, but prediction accuracy decreases due to evolving user behavior and limited data
Solution Approach 1:
The patent transitions from static models to dynamic models that continuously learn from new data. The system updates its understanding of user behavior and network patterns over time, allowing predictions to adapt to evolving user preferences and network dynamics, thereby improving prediction accuracy without requiring overly complex model structures.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from actual invitation outcomes and user behavior patterns. This feedback loop allows the model to continuously improve its predictions by adjusting its parameters based on real-world data, enhancing accuracy while maintaining reasonable model complexity.
3Measurement precision
If machine learning models are trained using conventional techniques to predict non-user acceptance, then prediction capability improves, but large amounts of labeled training data are required which is time-consuming and expensive
Solution Approach 1:
The patent applies self-service by enabling the system to generate its own training data through automated clustering and pattern recognition. Instead of requiring manual labeling of training data, the system automatically identifies relevant patterns in network data and uses these to train predictive models, significantly reducing the time and resources needed for data preparation.
Solution Approach 2:
The patent performs preliminary clustering and feature extraction actions before training the predictive model. By pre-processing the data to identify meaningful patterns and group similar entities, the system reduces the amount of labeled data needed for training, as the preliminary organization of data provides a foundation that simplifies subsequent modeling.
4Device complexity
If predefined features are used for classification to determine non-user acceptance, then classification process is simpler, but accuracy decreases as these features do not capture primary factors affecting decision
Solution Approach 1:
The patent changes the parameters used for classification from predefined static features to dynamically generated features based on actual network data. The system extracts meaningful parameters from connection patterns, interaction histories, and network position, which better capture the factors influencing non-user acceptance decisions while maintaining a manageable classification process.
Data Source
AI summary
The present disclosure relates to dynamic targeting of network invitations. Embodiments include clustering, based on network usage data, a plurality of in-network entities into active network users and passive network users. Embodiments include generating, for each active in-network entity, a vector representation based on connections between the active in-network entity and one or more other entities. Embodiments include generating, for each out-of-network entity, a corresponding vector representation based on connections between the out-of-network entity and one or more in-network entities. Embodiments include determining, for each out-of-network, a probability that the out-of-network entity will join the network based on comparing the corresponding vector representation of the out-of-network entity to a vector that is determined based on the vector representation of each active in-network entity. Embodiments include selecting an out-of-network entity to invite to the network based on the probability that the out-of-network entity will join the network.