Personalized Search via User Content Analysis
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Solution Overview
Problem
Creating and maintaining personal profiles for users in search engines is time-consuming and becomes outdated as user interests change, making it inefficient for personalized search results.
Innovation Solution
Implementing an adaptive, on-line personalized search system that uses personal content already part of the data corpus, such as authored, received, or participated content associated with the user's identifier, to enhance search relevance without relying on pre-created profiles, employing a two-phase search process with query expansion based on relevance feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personal profiles are created and maintained for users, then search result personalization is improved, but time consumption and maintenance complexity increase
Solution Approach 1:
The system automatically extracts user interests and preferences from the user's own content (authored, received, or participated content) without requiring manual profile creation or updates. The search engine autonomously builds and maintains the personalization model by analyzing content associations with the user's identifier, eliminating the need for users to spend time creating or updating profiles while still achieving personalized search results
Solution Approach 2:
The system continuously monitors and analyzes user content additions and modifications, using this feedback to dynamically update the personalization model. When new content is associated with the user's identifier, the system automatically incorporates this information into the user profile, ensuring the personalization remains current without manual intervention
2Reliability
If personal profiles are updated frequently, then search result relevance is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system automatically detects when new content is added or modified that is associated with the user's identifier and triggers profile updates only when necessary. This event-driven approach maintains search result relevance by keeping profiles synchronized with current user interests, while avoiding unnecessary updates that would increase system complexity and resource consumption
Solution Approach 2:
The personalization model is dynamically updated based on real-time content changes rather than following a fixed update schedule. The system adapts the profile maintenance frequency and intensity based on user activity levels, updating more frequently when users are actively adding content and less frequently during periods of inactivity, thereby balancing relevance with system efficiency
Data Source
AI summary
A query is received to search data, where the query includes a search term. A search of the data is performed in response to the query, wherein the search produces result data based on the search term and an identifier of a searcher submitting the query.