User Identification for Targeted Advertising Using Missing Data
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Solution Overview
Problem
Internet users are often exposed to irrelevant or offensive advertising on web sites, leading to decreased user interest and potential avoidance of sites, as existing methods fail to effectively tailor advertising to individual user interests.
Innovation Solution
A method and system that identifies users for targeted advertising by analyzing user data from Internet Service Providers and questionnaires, combining attribute values to determine user interests, and processing this information to select appropriate advertising content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertising material is displayed on web sites to generate revenue, then the provider can earn income, but the advertising may be irrelevant or offensive to users, causing them to ignore it or avoid future visits
Solution Approach 1:
The patent applies local quality by customizing advertising content according to individual user characteristics and preferences. User profiles are created based on demographic data, browsing behavior, and expressed interests, allowing advertisements to be tailored to each user's specific interests rather than using a uniform approach. This ensures that advertising material is relevant to each user locally, preventing irritation while maintaining effectiveness.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting advertising parameters such as content type, timing, frequency, and placement based on user attributes and behavior patterns. The system modifies advertising delivery parameters in real-time according to user profiles, ensuring that advertisements are presented in the most appropriate form and context for each user, thereby avoiding offense while maximizing impact.
2Adaptability or versatility
If user data is collected and analyzed to tailor advertising, then advertising relevance to users improves, but the complexity of data processing and user profiling increases
Solution Approach 1:
The patent applies segmentation by dividing user data into distinct categories and profiles based on demographic characteristics, browsing behavior, and interests. User data is segmented into manageable profile components that can be independently analyzed and applied to advertising decisions. This segmentation reduces processing complexity by organizing data into structured, reusable units rather than handling raw data as a monolithic whole.
Solution Approach 2:
The patent implements preliminary action by pre-processing user data to create comprehensive profiles before advertising campaigns begin. User attributes, preferences, and behavior patterns are analyzed in advance to build ready-to-use profiles that guide advertising delivery. This preliminary profiling reduces real-time processing complexity during actual advertising delivery, as the heavy analytical work has already been completed.
3Measurement precision
If multiple data sources including questionnaires are used to determine user attributes, then the accuracy of user interest identification improves, but the time and effort required for data collection increases
Solution Approach 1:
The patent applies merging by combining multiple data sources including questionnaire responses, browsing behavior data, demographic information, and purchase history into unified user profiles. These diverse data sources are integrated and cross-referenced to create a comprehensive view of user interests, improving measurement precision through triangulation of multiple indicators rather than relying on a single data source.
Solution Approach 2:
The patent implements self-service by allowing users to voluntarily complete questionnaires and provide preference information when it benefits them, such as receiving more relevant advertising or personalized content. Users self-report their interests and preferences, reducing the need for extensive data collection efforts while maintaining high accuracy. The system leverages user willingness to share information for their own benefit, transforming data collection from a burden into a mutually beneficial exchange.
Data Source
AI summary
A method and system for identifying users for advertising. Users, attributes, and first web sites provided by ISPs accessed by the users are identified. First data including content of the first web sites and user time spent thereon are received from ISPs and analyzed to determine first attribute values indicative of user interest. Second data received from ISPs include content of second web sites and user time spent thereon. Second attribute values, derived from questionnaires completed by the users, indicate interest in each attribute by each user. Third attribute values are determined by combining the first and second attribute values. The third attribute values are processed to identify users to which a product or service may be advertised. The identified users are communicated to a service provider or product provider. The first, second, or third attribute values may have missing values, which are determined by correlation and linear regression.