Dynamic User Profile Indexing for Ad Retrieval
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Solution Overview
Problem
Conventional advertisement retrieval systems face challenges in selecting relevant advertisements due to strict keyword matching, static user profiles, and inaccurate user information, leading to unsuitable ad presentation.
Innovation Solution
A system that analyzes user behavior through log data to create dynamic indices associating users with relevant keywords and advertisements, using Cookie-keyword and keyword-advertisement indices to personalize ad selection based on past search behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strict keyword matching is used to select advertisements, then the matching precision is improved, but the adaptability deteriorates because no advertisements can be identified when there is no precise keyword match
Solution Approach 1:
The system changes the matching parameter from exact keyword matching to semantic similarity matching. By using word similarity algorithms and semantic analysis, the system can identify relevant advertisements even when exact keywords do not match, thus resolving the contradiction between matching precision and adaptability
Solution Approach 2:
The system introduces semantic analysis and word similarity algorithms as intermediary mechanisms between the query keywords and advertisement keywords. This intermediary layer enables indirect matching based on semantic relationships, allowing the system to bridge gaps when exact keyword matches are absent
2Device complexity
If static user profiles are used to determine advertisements, then the system complexity is reduced, but the reliability deteriorates because the profiles do not reflect current user preferences
Solution Approach 1:
The system transitions from static user profiles to dynamic user models that continuously update based on real-time user behavior data. By making the profile dynamic and adaptive, the system maintains reliability in reflecting current user preferences without significantly increasing complexity through automated behavior analysis
Solution Approach 2:
The system implements feedback mechanisms where user behavior data (search queries, clicks, views) is continuously fed back into the user profile updating process. This feedback loop ensures the profile remains accurate and reflective of current preferences while using automated algorithms to manage complexity
3Measurement precision
If user information is collected through forms, then the information accuracy can be improved, but the loss of time increases due to the need for users to fill out forms
Solution Approach 1:
The system enables self-service information collection by automatically gathering user behavior data from search queries, clicks, and views without requiring users to fill out forms. The system serves itself by inferring user preferences from natural usage patterns, eliminating the time loss associated with form filling while maintaining information accuracy
Solution Approach 2:
The system replaces the mechanical form-filling process with automated digital behavior analysis. Instead of relying on users manually providing information through forms, the system uses computational algorithms to extract and analyze user behavior patterns, significantly reducing time requirements while maintaining or improving information accuracy
Data Source
AI summary
Method and system for retrieving advertisement information. Information characterizing a user's past search behavior is utilized in creating index data that associate individual users to advertisements. When a search request from a user is received, the index data are utilized to identify one or more advertisements with respect to the received user's search request. Such identified advertisements match the user's past search behavior.