Personalized Meta-Search System Using Interest Database
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Solution Overview
Problem
Existing search engines struggle to accurately comprehend user intentions due to reliance on keyword-based retrieval methods, leading to unsatisfactory search results, especially on mobile terminals where user-specific data is abundant and personalized search experiences are lacking.
Innovation Solution
A personalized meta-search method utilizing an interest database and user logs to preprocess and re-rank search results, calculating interests based on keyword relevance, click history, and browsing behavior, providing more targeted and satisfactory search outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If keyword-based retrieval is used, then search speed is improved, but search result relevance deteriorates
Solution Approach 1:
The patent segments the search result processing into multiple stages: initial keyword-based retrieval, interest calculation based on user logs, and re-ranking based on calculated interests. This segmentation allows the system to maintain fast keyword retrieval while adding relevance improvement through subsequent processing steps.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing user interests in an interest database before actual search execution. User logs are continuously analyzed and stored, so when a search occurs, the system can quickly retrieve pre-computed interest data to enhance search results without adding significant delay.
2Measurement precision
If personalized search processing is added, then search result relevance is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces an interest database as an intermediary component that stores pre-computed user interests. This intermediary layer simplifies the personalized search process by providing ready-to-use interest data, avoiding the need for complex real-time analysis during search execution.
Solution Approach 2:
The system implements self-service by automatically analyzing user logs and updating the interest database without requiring manual intervention. The interest calculation mechanism autonomously processes user behavior data and maintains the interest database, reducing operational complexity.
3Measurement precision
If user logs are continuously analyzed, then personalization accuracy is improved, but data processing time deteriorates
Solution Approach 1:
The system employs periodic action by analyzing user logs at intervals rather than in real-time for every search query. User behavior data is collected and processed periodically to update the interest database, balancing personalization accuracy with processing time efficiency.
Solution Approach 2:
User interests are pre-calculated and stored in the interest database before searches are executed. This preliminary processing of user log data allows the system to have personalization data ready in advance, eliminating the need for time-consuming analysis during actual search operations.
Data Source
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AI summary
The disclosure discloses a personalized meta-search method, which comprises: preestablishing an interest database; searching a keyword; extracting a use log of the keyword from the interest database; preprocessing search results returned from a search engine; calculating, by using the use log, interests of the preprocessed search results and of Uniform Resource Locators (URLs) in the interest database corresponding to the searched keyword; ranking, based on calculated results, and displaying the preprocessed search results and the URLs; updating the interest database. An application terminal for personalized meta-search is also disclosed. The method and the application terminal enable users to obtain more targeted and more satisfactory search results, thereof improving user experience.