Text Search Weighting for O2O Query Relevance
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Solution Overview
Problem
Existing search methods on O2O platforms face challenges in providing comprehensive and accurate search results due to numerous irrelevant text index fields, leading to inefficient information retrieval.
Innovation Solution
A search method that determines specific search policies matching query texts, assigning weights to relevant text index fields, and merging results from these policies to enhance accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all text index fields are searched using web page search method, then comprehensive information coverage is achieved, but search accuracy deteriorates due to numerous irrelevant fields
Solution Approach 1:
The patent segments the search process by dividing text index fields into different categories (first text index fields and second text index fields) based on their relevance to the query. This segmentation allows the system to apply different search strategies to different field types, searching only relevant fields thoroughly while skipping irrelevant ones, thus maintaining information coverage while improving search accuracy.
Solution Approach 2:
The patent applies local quality by assigning different search weights to different text index fields based on their relevance to the query. First text index fields (relevant fields) are assigned higher search weights while second text index fields (irrelevant fields) are assigned lower or zero weights. This localized quality adjustment ensures that search resources are concentrated on relevant fields, improving overall search accuracy without losing comprehensive information coverage.
2Quantity of substance
If search is performed on all text index fields, then information completeness is improved, but search efficiency deteriorates
Solution Approach 1:
The patent extracts and identifies the subset of relevant text index fields (first text index fields) from the complete set of all text index fields based on query analysis. By extracting only the relevant fields for searching while excluding irrelevant fields (second text index fields), the system maintains information completeness for relevant aspects while significantly improving search efficiency by reducing the search space.
3Measurement precision
If search weights are assigned to text index fields, then search accuracy is improved, but system complexity increases
Solution Approach 1:
The patent changes the parameter of search weight assignment based on the classification of text index fields. By defining clear rules for assigning weights (higher weights to first text index fields, lower or zero weights to second text index fields), the system improves search accuracy through parameter adjustment while keeping the complexity manageable through systematic and rule-based weight determination rather than complex algorithms.
Data Source
AI summary
A search method includes: determining at least one first search policy matching a to-be-searched query text, where each first search policy corresponds to at least one first text index field and a search weight matching the first text index field; performing a search operation for the query text based on each first text index field corresponding to each first search policy and a search weight matching the first text index field; and merging and outputting search results of all the search operations.