Snippet Generation Based on User Interest Context
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Solution Overview
Problem
Current search systems fail to effectively personalize search query results by accounting for users' long-standing and recent interests, leading to irrelevant snippets in search results.
Innovation Solution
A system and method that generate and refine snippets based on user interests by analyzing stored data, ranking entities in documents, and providing customized snippets that align with user queries, incorporating user history and social media data to prioritize relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search systems provide generic snippets for all users, then the system complexity is low, but the relevance to individual user interests deteriorates
Solution Approach 1:
The patent segments the search result presentation by providing different snippets to different users based on their interests. The system divides the generic search result into customized versions by selecting and annotating specific portions of documents that align with individual user interests, thereby increasing relevance without requiring complete system redesign
Solution Approach 2:
The system performs preliminary analysis of user interest data (search history, profile information) before generating search results. By pre-processing and storing user interest profiles, the system can quickly retrieve and apply relevant interests during search operations, reducing the complexity of real-time personalization
2Measurement precision
If the system analyzes and processes user data to generate personalized snippets, then the relevance of search results improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes user data (search history, profile information) and stores it in an accessible format before search operations occur. This preliminary preparation allows the system to quickly retrieve user interests during search without performing heavy analysis in real-time, thus maintaining low processing delay
Solution Approach 2:
The system creates simplified representations or copies of user interest profiles that can be quickly compared against search documents. Instead of analyzing complete user histories during each search, the system uses pre-generated interest profiles as proxies, reducing computational overhead while maintaining personalization effectiveness
3Productivity
If the system provides detailed customized snippets, then user engagement improves, but the amount of information processing and data storage requirements increase
Solution Approach 1:
The system extracts only the specific portions of documents that are relevant to user interests rather than processing or storing entire documents. By selecting and annotating only the relevant snippets, the system provides detailed customization without requiring proportional increases in data storage capacity
Solution Approach 2:
The system applies customization locally to specific portions of search results rather than transforming entire documents. By annotating and modifying only the relevant snippets while leaving the rest of the document structure intact, the system enhances user engagement without proportionally increasing processing and storage requirements
Data Source
AI summary
A system obtains a document that is responsive to a user query. The system determines an interest of the user based on stored data associated with the user, and determines that a portion of the document relates to the interest of the user. The system generates a first snippet for the document based on the portion of the document that relates to the interest of the user, and provides the first snippet for the document as part of a result list.