Personalization Search Engine Ranking via User Feedback
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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 similar search results for users with different preferences or locations, and do not sort indexed information based on user activities or content created by users with similar tastes.
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 online activities, even for anonymous users, and considers geographical location and sentiment analysis to provide localized and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional web search engines use standard search algorithms to process queries, then search results are generated quickly and consistently, but the results are not personalized to individual user preferences and online activities
Solution Approach 1:
The patent segments the search result generation process into multiple independent components: a conventional search engine component that handles basic query processing, and a personalization component that analyzes user feedback and online activities. These segments work together through integration, allowing the system to maintain the simplicity of conventional search while adding personalization capabilities through modular architecture
Solution Approach 2:
The patent introduces an intermediary personalization engine that acts as a mediator between the conventional search engine and the user. This intermediary component analyzes user feedback, ratings, and online activities, then modifies search results by re-ranking or filtering them based on personalized preferences, without replacing the entire search system
2Measurement precision
If web search engines analyze user feedback and online activities to personalize results, then search result relevance improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-analyzing and storing user feedback, ratings, and online activities in a user profile database before search queries are submitted. This pre-processing allows the personalization engine to quickly retrieve and apply relevant user preferences during search operations, rather than analyzing all user data from scratch for each query
Solution Approach 2:
The patent applies partial action by selectively analyzing only the most relevant user feedback and online activities related to the current search query topic, rather than processing all user data. The system identifies and focuses on specific user preferences that are most applicable to the query context, reducing unnecessary computational overhead
3Adaptability or versatility
If search engines consider geographical location and user preferences for personalization, then local relevance improves, but the system requires more data collection and processing
Solution Approach 1:
The patent extracts and separates geographical location data and user preference data into distinct, independently processable components. The system extracts location information from user profiles or device data, and separately analyzes user feedback and online activities, then combines these extracted elements to generate personalized local results without requiring all data to be processed as a single large dataset
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.