Smart Merge Interleave Algorithm for Search Result Relevance
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Solution Overview
Problem
Conventional help applications merge online and local search results in a one-to-one fashion, which can hurt relevance by placing poor local results higher in the merged list, as they cannot compare the weights given by different search algorithms for online and local content sources.
Innovation Solution
A smart merge interleave algorithm that determines the correspondence between online and local results, creating an interleaved list where local results are merged in relevant positions without affecting the relevance, using a longest common subsequence approach to handle heterogeneous content sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional one-to-one merge process is used to combine online and local search results, then the merging process is simple and fast, but the relevance of search results deteriorates because poor local results may appear at higher positions in the merged list
Solution Approach 1:
The patent changes the merging parameters by introducing weight comparisons between online and local results. Instead of simple one-to-one merging, the system evaluates relevance weights and uses dynamic merging strategies (standard merge, aggressive merge, conservative merge) based on weight thresholds, thereby improving result relevance while maintaining reasonable merging efficiency
Solution Approach 2:
The patent implements dynamic merging behavior that adapts based on result weights. The merge process is no longer static one-to-one but dynamically adjusts which results to include and in what order, allowing poor local results to be filtered out when their weights are insufficient compared to online results
2Reliability
If weight comparison between online and local results is performed to improve relevance, then the search result relevance is improved, but the device complexity increases due to the need for weight evaluation and comparison mechanisms
Solution Approach 1:
The patent segments the merging process into distinct phases: weight evaluation phase, threshold comparison phase, and result assembly phase. It also segments merging into different strategies (standard, aggressive, conservative) that can be selected based on needs, making the complex process more manageable and controllable
3Measurement precision
If duplicate results are removed to improve precision, then the search result precision is improved, but the loss of information may occur when valid local results are incorrectly excluded
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously evaluates result weights and adjusts merging behavior accordingly. Weight thresholds are established and fed back into the merging process to determine inclusion/exclusion, ensuring that valid local results are not incorrectly excluded while still removing true duplicates
Data Source
AI summary
Search result interleaving may be provided. First, a first list comprising online results may be received. Next, a second list comprising local results may be received. Then a subsequence common to the first list and the second list may be determined using a longest common subsequence algorithm. Next, an interleaved list may be created using the determined subsequence and a smart merge interleave algorithm to interleave the first list and the second list without hurting relevance.