Mining Offline Resources via User Search Log Matching
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Solution Overview
Problem
Existing solutions for extracting offline merchant resources in O2O commerce are incomplete in analysis dimensions, rely on one-sided data sources, and lack timeliness, failing to accurately reflect consumer demands.
Innovation Solution
A method and apparatus that acquire user search log information to extract user demand characteristics, matching them with a defined offline resource set to provide relevant offline resource information, utilizing big data for improved coverage and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing solutions use historical data or competitive product data to extract merchant resources, then the process is simple to implement, but the analysis dimensions are incomplete and data timeliness is poor
Solution Approach 1:
The patent introduces a new dimension of user search behavior data to complement existing historical data and competitive product data. By incorporating search query information, browsing patterns, and click-through data, the system creates a multi-dimensional analysis framework that captures both supply-side (merchant) and demand-side (user) perspectives, resolving the incompleteness of traditional single-dimension approaches
Solution Approach 2:
The system establishes a feedback loop by continuously collecting user search log data and using it to refine merchant resource extraction. User search behaviors provide real-time feedback on actual consumer demands, which is then fed back into the merchant selection and matching process, enabling dynamic optimization rather than relying solely on static historical data
2Ease of manufacture
If existing solutions rely on historical data within a certain period, then data collection is straightforward, but data timeliness is insufficient
Solution Approach 1:
The patent implements continuous data collection from user search logs and online behaviors, replacing periodic historical data snapshots with an ongoing stream of real-time information. This continuous monitoring ensures that merchant resource extraction reflects current user demands and market conditions, eliminating the time lag inherent in periodic data collection methods
Solution Approach 2:
The system performs preliminary analysis of user search patterns and demands before merchant resource extraction. By pre-processing and analyzing user behavior data in advance, the system prepares timely insights that can be quickly applied to merchant selection, reducing the overall time required to generate up-to-date merchant resources
3Device complexity
If existing solutions use one-sided data sources, then data processing is simplified, but actual consumption demands of users are neglected
Solution Approach 1:
The patent merges multiple data sources including user search logs, browsing behaviors, click-through data, and traditional merchant information into a unified analysis framework. This integration combines supply-side merchant data with demand-side user behavior data, creating a comprehensive view that captures both merchant capabilities and actual consumer demands, thereby resolving the one-sidedness of existing solutions
Solution Approach 2:
The system creates a multi-functional data processing framework that simultaneously analyzes merchant attributes, user preferences, search patterns, and conversion metrics. This universal approach enables the system to handle diverse data types and answer multiple questions about merchant-resource matching, rather than being limited to single-purpose analysis
Data Source
AI summary
The present disclosure discloses a method and apparatus for mining offline resources. The method includes: acquiring at least two pieces of user search log information; acquiring a user demand characteristic set according to a search formula included in the user search log information, the user demand characteristic set including a keyword set; and matching the user demand characteristic set with a defined offline resource set, to acquire offline resource information corresponding to the user demand characteristic set.