Personalized Content Generation via Transaction Data Analysis
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Solution Overview
Problem
Travel planning websites rely on subjective and often biased user reviews, which can be unreliable and time-consuming for users to sift through, leading to inaccurate recommendations and a lack of personalization in content delivery.
Innovation Solution
A system that generates custom content based on user transaction history data, using closed-loop data to create user profiles and merchant scores, providing personalized recommendations by analyzing transaction patterns and user feedback to reduce bias and enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user reviews are used to generate recommendations, then personalization of content is achieved, but reliability and accuracy of recommendations deteriorate due to subjective bias and manipulation
Solution Approach 1:
The patent introduces an intermediary system that processes user reviews through natural language processing and sentiment analysis to extract objective insights. This intermediary layer filters out biased or manipulative content while preserving genuine user preferences, thereby maintaining personalization accuracy without relying directly on raw user reviews.
Solution Approach 2:
The patent replaces the mechanical system of direct user review aggregation with an automated computational system using machine learning algorithms. This system objectively analyzes transaction data and review patterns to generate recommendations, eliminating human subjectivity and manipulation while maintaining personalization capabilities.
2Quantity of substance
If voluminous reviews are provided to users, then comprehensive information is available, but time required to find relevant content increases
Solution Approach 1:
The patent extracts only the most relevant and actionable information from voluminous reviews using natural language processing. It identifies key sentiment patterns, common complaints, and positive attributes, presenting condensed insights to users rather than requiring them to read through all original reviews, thus reducing time investment while maintaining information quality.
Solution Approach 2:
The system performs preliminary analysis of reviews before presentation to users, pre-processing and categorizing information by relevance, sentiment, and importance. This preliminary action organizes voluminous data into digestible formats, allowing users to quickly access pertinent information without sifting through unnecessary content.
3Measurement precision
If affirmative actions are required from users to provide personalization data, then accuracy of user profile is improved, but ease of operation deteriorates due to setup complexity
Solution Approach 1:
The patent enables the system to automatically collect and analyze user transaction data without requiring explicit user input or affirmative actions. The system self-serves by processing purchase history, browsing behavior, and interaction patterns to build accurate user profiles, eliminating the need for users to manually provide personalization data while maintaining profile accuracy.
Solution Approach 2:
The system performs preliminary data collection and analysis during normal user transactions, building user profiles in the background before personalization is needed. This preliminary action accumulates sufficient user data automatically, so when personalization is required, the system already has accurate information without requiring additional user effort or setup steps.
Data Source
AI summary
A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to display a first plurality of search results on a user interface of a user device based at least in part on a first request. Clickstream data can be identifier from the user interface displayed on the user device. The clickstream data comprises a user-specified sort of the first plurality of search results on the user interface. A sorting habit for the user can be determined based at least in part on the user-specified sort of the first plurality of search results on the user interface. The computing device can display on the user interface a second plurality of search results. The second plurality of search results are sorted for display based at least in part on the sorting habit for the user.