Synonym Identification via Interaction Analysis
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Solution Overview
Problem
Current search technologies face challenges in efficiently indexing and categorizing document content across wide area networks, particularly in supporting searches for synonyms, as they often require multiple keyword searches and may not effectively utilize interaction data to identify similar items.
Innovation Solution
The method involves analyzing interaction records between visitors and server-based systems to identify synonyms by comparing input documents to existing documents, establishing similarity ratings, and merging detected synonyms into search indexes to enhance search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple keyword searches are performed to support synonym searches, then search completeness is improved, but search time and system complexity increase
Solution Approach 1:
The system performs preliminary action by analyzing user interactions and identifying synonym relationships between items before search queries are executed. Similarity ratings are established in advance based on interaction patterns, so when a search is performed, the system can quickly retrieve pre-identified synonyms rather than conducting multiple separate keyword searches. This eliminates the need for runtime synonym expansion and significantly reduces search time while maintaining complete result coverage.
2Reliability
If multiple keyword searches are performed to support synonym searches, then search completeness is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically analyzing user interactions and identifying synonym relationships without requiring manual configuration or complex external systems. The interaction analysis component continuously monitors user behavior patterns and autonomously builds the similarity rating structure, eliminating the need for manual synonym databases or complex multi-system architectures. This self-organizing approach reduces system complexity while maintaining comprehensive synonym coverage.
3Measurement precision
If interaction data is analyzed to identify synonyms, then search accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system applies the extraction principle by isolating and analyzing only the specific interaction data elements that are relevant to synonym identification. Rather than processing all available data, the system extracts key interaction patterns (such as substitution behaviors, co-occurrence patterns, and selection sequences) and focuses computational resources on these extracted signals. This selective extraction approach maintains high search accuracy by focusing on relevant patterns while reducing overall data processing complexity.
Data Source
AI summary
Tools and techniques are described for analyzing interactions to identify dissimilar items that may contain synonyms. Methods described herein may retrieve activity records that represent interactions between a visitor and a server-based system, and may identify within the activity records inputs that the visitor provided during the interaction. The methods may identify items within the activity record that are associated with the inputs, and may access additional activity records that also contain the same inputs. The methods may then identify additional items within the additional activity records that are associated with this same input, and may establish similarity ratings for the two items, with the similarity ratings indicating a likelihood that documents respectively associated with the items contain synonyms.