Social Network Search Relevance via Conversation Indexing
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Solution Overview
Problem
Existing social network systems struggle to effectively search and retrieve relevant conversations within large datasets, often prioritizing document length over relevance and failing to accurately represent conversation relevance in search results.
Innovation Solution
A social network system that generates social objects from enterprise business applications, indexes conversations as documents, and uses keyword search to provide a relevance-ranked result set, considering conversations as a unified document for accurate scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system indexes and searches conversations as individual messages or fragmented data, then the search coverage is comprehensive, but the relevance accuracy deteriorates because the system cannot capture the contextual meaning of complete conversations
Solution Approach 1:
The patent merges multiple individual messages into a unified conversation document structure. Each conversation is treated as a single searchable entity containing all related messages, allowing the search system to evaluate relevance at the conversation level rather than message level. This combining approach preserves contextual meaning while enabling accurate relevance ranking.
Solution Approach 2:
The patent segments the overall conversation dataset into distinct conversation documents, each representing a complete conversational thread. This segmentation allows the search system to process and rank individual conversations as discrete units, improving relevance accuracy by maintaining the integrity of conversational context without requiring analysis of the entire dataset at once.
2Loss of information
If the system prioritizes document length in search ranking, then the completeness of information is improved, but the relevance accuracy deteriorates because longer documents are not necessarily more relevant
Solution Approach 1:
The patent changes the ranking parameter from document length to conversation-level relevance scoring. Instead of prioritizing longer documents, the system evaluates each conversation document's relevance to the search query using algorithms that consider keyword matching, contextual factors, and conversation metadata. This parameter change ensures that shorter but highly relevant conversations are ranked above longer but less relevant ones.
3Ease of manufacture
If the system treats conversations as fragmented messages, then the indexing process is simpler, but the search result relevance deteriorates because contextual relationships are lost
Solution Approach 1:
The patent applies preliminary action by pre-processing conversations into structured conversation documents before indexing. During this preliminary step, the system organizes individual messages into coherent conversation units, assigns metadata, and establishes document structures. This upfront preparation maintains indexing simplicity while ensuring that the indexed units preserve contextual relationships for accurate search results.
Data Source
AI summary
A social network system that provides a keyword search result set from content within the social network receives from an enterprise application a business object and an associated system of record, and generates a social object for the social network, where the social object corresponds to the business object and associated system of record. The system generates a plurality of conversations related to the social object, where each conversation comprises content. The system indexes each of the conversations as a conversation document and receives a keyword search term. The system then searches for the keyword search term within the conversation documents, and generates a result set of relevant conversations in response to the searching, where the relevant conversations are displayed in order of relevance.