Automatic Vertical Search Engine Recommendation
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Solution Overview
Problem
Users often fail to utilize topic-specific search engines effectively due to unawareness of their existence, leading to suboptimal search results when using general-purpose search engines alone.
Innovation Solution
An automatic search engine recommendation technique that matches user queries with suitable vertical search engines by creating a model using features such as query data, user behavior, and geographic location, and ranks these engines for recommendation alongside general-purpose search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users rely only on general-purpose search engines, then the search system remains simple and easy to use, but search effectiveness and result quality deteriorate due to lack of topic-specific expertise
Solution Approach 1:
The patent introduces an intermediary recommendation system that sits between the user and multiple search engines. This intermediary automatically analyzes user queries, identifies relevant vertical search engines, and recommends them to users without requiring users to manually evaluate or compare different search engines. The intermediary handles the complexity of selecting appropriate search engines, allowing users to benefit from topic-specific expertise while maintaining simple interaction.
Solution Approach 2:
The recommendation system performs self-service by automatically analyzing user queries, identifying relevant vertical search engines, and presenting recommendations without requiring user input or manual configuration. The system autonomously determines which vertical search engines match user needs based on query analysis and pre-established mappings between queries and vertical search engines.
2Reliability
If users are provided with multiple vertical search engine options, then search result quality improves through topic-specific expertise, but user operation complexity increases due to difficulty in selecting the appropriate engine
Solution Approach 1:
The recommendation system acts as an intermediary that automatically bridges users and appropriate vertical search engines. It analyzes user queries, matches them with relevant vertical search engines using pre-established mappings, and presents targeted recommendations. This eliminates the need for users to manually evaluate multiple vertical search engines or understand their differences, maintaining ease of operation while improving result quality.
Solution Approach 2:
The system performs preliminary action by pre-establishing mappings between queries and vertical search engines before users need them. During user interactions, the system quickly retrieves pre-analyzed recommendations based on the current query, avoiding real-time complexity and enabling instant, easy-to-follow suggestions without requiring users to make complex decisions.
3Measurement precision
If the system recommends vertical search engines based on detailed feature analysis, then recommendation accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing mappings between queries and vertical search engines before they are needed. When users submit queries, the system quickly retrieves pre-established recommendations based on the current query and usage data, avoiding time-consuming real-time analysis while maintaining high recommendation accuracy through pre-performed feature analysis.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on usage data collected over time. By monitoring which vertical search engines users actually use and prefer, the system refines its recommendations and updates mappings to reflect real-world effectiveness. This allows the system to maintain high accuracy by adapting to changing user preferences and search patterns without requiring constant re-analysis of all features.
Data Source
AI summary
The automatic search engine recommendation technique described herein automatically recommends topic-specific search engines for user queries. In one embodiment, it automatically matches each query submitted to a non-topic specific or general search engine with one or more vertical search engines using a recommendation model and a set of features. For a given query, one embodiment of the technique suggests vertical search engines and topic-specific search results along with the search results from the general search engine.