Cross-Platform Search Token Generation for Personalized Content
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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 user experience, as existing systems fail to predict and present relevant search queries based on user behavior across different digital platforms.
Innovation Solution
A system that generates search tokens for users by aggregating data from multiple 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
1Loss of time
If users manually search for topics on search engines, then they can find information, but they often forget to search and lose time
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns across digital platforms and pre-generating search tokens before the user actually needs to search. This allows the system to predict and prepare search queries in advance, reducing the time users would otherwise lose by forgetting to search.
Solution Approach 2:
The system enables self-service by automatically generating and presenting search tokens to users without requiring manual input. The system serves itself by using its own behavior analysis capabilities to create personalized search predictions, eliminating the need for users to manually initiate search queries.
2Loss of energy
If merchants spend money on broad advertising, then they can reach more users, but they waste marketing budget on untargeted audiences
Solution Approach 1:
The system applies local quality by delivering highly targeted advertisements to specific user segments based on their behavior patterns and predicted search tokens. Instead of uniform broad casting, the system tailors ad content and timing to match individual user interests and needs, improving marketing budget efficiency while maintaining high customer acquisition effectiveness.
Solution Approach 2:
The system performs preliminary analysis of user behavior and predicts future search intentions before delivering advertisements. This allows merchants to target users at the optimal moment when they are most likely to be interested in the advertised products, reducing wasted ad spend on uninterested audiences while maintaining high conversion rates.
3Measurement precision
If the system collects data from multiple digital platforms, then it can build comprehensive user profiles, but it increases system complexity
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that can collect, process, and analyze data from multiple digital platforms simultaneously. The unified system performs various functions including behavior tracking, pattern recognition, search token generation, and advertisement targeting, reducing the need for separate systems while maintaining high measurement precision.
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.