Cross-Platform User Profile Vectors for Predictive Content Presentation
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Solution Overview
Problem
Users often forget to search for topics of interest on search engines, leading to lost revenue for merchants and reduced business opportunities, and existing systems fail to efficiently predict and deliver targeted content based on user behavior across multiple platforms.
Innovation Solution
A system that generates search tokens for users by aggregating data from various digital platforms, building a user profile vector, and using deep learning techniques to predict and rank content of interest, enabling hyper-personalized advertisements and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques and reinforcement learning are used to improve content recommendation algorithms, then recommendation accuracy and user engagement are improved, but system complexity and computational resources required increase
Solution Approach 1:
The system segments user data collection and processing into multiple components: collecting user activities across digital platforms, building user profile vectors, and using learning modules to generate search tokens. This segmentation allows complex deep learning operations to be broken down into manageable stages, improving recommendation accuracy while controlling system complexity through modular architecture.
2Measurement precision
If user behavior data is collected from multiple digital platforms to build comprehensive user profiles, then content targeting precision is improved, but data processing complexity and privacy concerns increase
Solution Approach 1:
The system introduces a user profile vector as an intermediary representation that aggregates user behavior data from multiple digital platforms. Instead of directly processing raw data from various sources, the system transforms diverse user activities into a unified vector format, simplifying subsequent analysis and reducing data processing complexity while maintaining high content targeting precision.
3Productivity
If actionable advertisements are targeted at users with anticipated needs, then marketing budget efficiency is improved, but prediction accuracy requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting user behavior data and building user profile vectors in advance. This allows the learning module to generate search tokens and predict user needs before users actively search, enabling proactive actionable advertisements to be delivered with high accuracy, thereby improving marketing budget efficiency.
4Ease of operation
If search queries are predicted based on user behavior across the internet, then user convenience and time savings are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system implements self-service by automatically generating search tokens based on user behavior patterns without requiring user intervention. The learning module continuously analyzes user activities across digital platforms and autonomously produces predicted search queries, improving user convenience while managing system complexity through automated processes.
Data Source
AI summary
A system for generating search tokens for a user is provided. The system comprises a server, wherein the server comprises one or more processors. The server is operable to receive and store one or more user information in a user database. Further, the server identifies one or more of profiles or accounts of the user on one or more digital platforms. The server then collects, and stores one or more information related to one or more activities of the user on the digital platforms and in external systems, in the user database. The server then builds a user profile vector to characterize the user's behavior. Further, the server processes the user profile vector with the help of a learning module in order to derive one or more search tokens. Subsequently, the server may rank the search tokens to identify one or more content that is of interest to the user.