Personalization Search Engine Using User Feedback Ranking
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Solution Overview
Problem
Conventional web search engines fail to provide personalized search results, as they do not consider the online activities and preferences of individual users, leading to non-personalized search results for users with different preferences or locations, despite entering the same search query.
Innovation Solution
A personalization search engine (PSE) that analyzes metadata from user feedback, such as reviews and ratings, to determine the relevance of search results to individual users, ranking objects based on their preferences and opinions, even for anonymous users, by identifying similar tastes and preferences from user-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional web search engines use traditional keyword matching and inverted indexing, then search speed and system simplicity are maintained, but search results lack personalization and relevance to individual user preferences
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user feedback, ratings, and online activities before search queries are submitted. User profiles and preference models are built in advance through continuous monitoring of user interactions, enabling personalized search results without adding complexity to the core search engine operations.
Solution Approach 2:
The patent introduces intermediary components including user profile databases, preference analysis modules, and relevance ranking systems that mediate between the basic keyword search function and the final personalized results. These intermediaries layer personalization capabilities without fundamentally redesigning the core search infrastructure.
2Adaptability or versatility
If the search engine analyzes user feedback and online activities to determine user preferences, then personalized search results are achieved, but data processing complexity and computational resources increase
Solution Approach 1:
The system segments the personalization process into distinct modular components: user feedback collection modules, preference analysis modules, profile storage databases, and result ranking modules. Each segment handles a specific aspect of personalization, making the overall complex system manageable and maintainable through clear separation of concerns.
Solution Approach 2:
The patent creates universal user profile structures and preference models that can be applied across different search queries and user types. The same infrastructure serves multiple functions including anonymous user profiling, registered user personalization, and adaptive result ranking, reducing redundant complexity.
3Measurement precision
If the search engine processes and ranks objects based on user feedback from multiple users, then search result accuracy for individual users is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing by pre-computing user profiles, preference vectors, and relevance weights from user feedback before actual search queries are executed. Aggregate user data and preference models are prepared in advance, enabling fast personalized ranking during query execution without real-time reprocessing of all user feedback.
Solution Approach 2:
The patent applies partial processing by focusing computational resources on the most relevant user feedback and preferences for each query rather than processing all available data equally. The system identifies and processes only the subset of user feedback that most significantly impacts result ranking for the given query context.
Data Source
AI summary
Methods and apparatus provide for a personalization search engine that receives a search query from a first user and identifies multiple portions of indexed content—where each respective portion of indexed content has metadata that matches at least one characteristic of the search query. The personalization search engine determines a relevance of each respective portion of indexed content to the first user who provided the search query. It is understood that, in various embodiments, the relevance of a portion of indexed content has to the first user who provided the search query can be based on user feedback associated with an online version of that portion of indexed content. The personalization search engine ranks the multiple portions of indexed content according to their respective relevance to the first user who provided the search query and creates a search result based on ranking the multiple portions of indexed content.