Search Query Refinement via Social and Retail Data Integration
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Solution Overview
Problem
Users face frustration in finding relevant search results due to the large number of search results produced by simple internet searches, and retailers struggle to appear in search results when their products or services are not appropriately indexed with relevant terms.
Innovation Solution
A computer system that receives entity data from online retailers and social network data from users, modifies search terms based on this information, and submits these modified terms to a search engine to enhance search results relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a simple Internet search is performed, then the search engine returns a large number of results, but users find it frustrating to comb through these results to find relevant products or services
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user data from social networks and entity data from retailers before the search is executed. This pre-processing of data allows the system to enhance search results with relevant information, making the search process more efficient and reducing the time users spend combing through irrelevant results.
Solution Approach 2:
The system acts as an intermediary between the user's simple search query and the search engine results. It receives the user's search terms, enhances them by incorporating collected user data and entity data, and then submits the enhanced terms to the search engine. This intermediary layer filters and refines the information flow, delivering more relevant results without requiring users to manually filter through large result sets.
2Speed
If traditional search indexing is used, then search results are generated quickly, but retailers struggle to appear in search results when their products or services are not appropriately indexed with relevant terms
Solution Approach 1:
The system implements feedback mechanisms by continuously collecting user data from social networks and entity data from retailers, then using this information to enhance search terms and improve result relevance. This feedback loop ensures that retailer information is continuously updated and optimized, increasing the reliability of retailer visibility in search results without sacrificing generation speed.
Solution Approach 2:
The system changes the parameters of search terms by incorporating additional data dimensions from user profiles and entity information. Instead of relying solely on traditional indexing parameters, the system enriches search queries with contextual information, making retailer appearance in search results more reliable while maintaining quick result generation through efficient data processing.
3Measurement precision
If user data and entity data are collected and processed, then search result relevance is improved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single platform that performs multiple tasks: collecting user data from social networks, gathering entity data from retailers, processing and analyzing this information, and enhancing search results. This universal approach improves search relevance while managing complexity through consolidated functionality rather than separate specialized systems.
Solution Approach 2:
The system employs self-service mechanisms by automatically collecting and processing user data and entity data without requiring manual intervention. The automated data collection from social networks and retailers, combined with automatic processing and enhancement of search terms, improves precision while minimizing the operational complexity that would arise from manual data management.
Data Source
AI summary
A system and method for enhancing search results is described. The system receives information and data about retailers and social network data about a user. The system then detects search terms input by the user into a search field (e.g., on a search webpage) and modifies the search terms at least partially based on the information associated with the various retailers and the social network data. The system (or the user) then submits the modified search terms to a search engine and receives search results based on the modified search terms.