Dialog Message Ranking via Fixed Point Operations
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Solution Overview
Problem
Existing systems for ranking messages in dialogs are inefficient due to their reliance on verbose query processing methods that fail to account for the unique characteristics of conversational exchanges, such as natural language text and sequential dependency, often returning irrelevant information and lacking sensitivity to specific search queries.
Innovation Solution
A system that receives a pseudo-relevance set from a search engine, executes a first fixed point operation to generate weighted terms, and a second fixed point operation on a message graph with nodes based on these weighted terms to produce a final message ranking, effectively addressing the inefficiencies by incorporating temporal bias and message similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If verbose query processing methods are used to rank messages in dialogs, then the system can process general search queries, but it fails to account for unique characteristics of conversational exchanges resulting in irrelevant information and lack of sensitivity to specific search queries
Solution Approach 1:
The patent applies local quality by treating different parts of the dialog with different weighting schemes. Specifically, it assigns temporal bias weights to messages based on their position in the conversation sequence, and computes message-specific importance scores that reflect their local relevance to the current search query. This allows the system to prioritize certain messages (e.g., recent ones or those containing key terms) while de-emphasizing others, thereby improving relevance without losing adaptability to conversational nuances
Solution Approach 2:
The patent segments the dialog into individual messages and represents each message as a separate node in a message graph. This segmentation allows the system to process and rank each message independently based on its own characteristics (such as temporal position, term frequency, and similarity to the query) rather than treating the entire dialog as a single verbose query. The message graph structure enables fine-grained control over which messages are retrieved and how they are ranked
2Measurement precision
If fixed point operations are executed on pseudo-relevance sets and message graphs to generate weighted terms and final rankings, then message ranking accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by first generating a pseudo-relevance set from the message graph before performing the computationally intensive fixed point operations. The system pre-computes term weights using term frequency-inverse document frequency (TF-IDF) weighting on the pseudo-relevance set, and pre-establishes the message graph structure with nodes representing messages and edges representing similarities. This preliminary processing reduces the complexity of the subsequent fixed point iterations by working with a smaller, pre-filtered subset of messages rather than the entire dialog
Solution Approach 2:
The patent implements partial action by performing fixed point operations only on the pseudo-relevance set (a subset of top-k messages) rather than on all messages in the dialog. The system retrieves only the most relevant messages based on initial scoring, and then applies the computationally expensive fixed point iterations only to this reduced set. This approach achieves high ranking accuracy for the most important messages while avoiding the prohibitive computational cost of processing the entire message set
Data Source
AI summary
An example system includes a processor to receive a pseudo-relevance set including top results form a search engine in response to transmitting a set of concatenated messages of a dialog. The processor can execute a first fixed point operation on the pseudo-relevance set to generate weighted terms. The processor can also execute a second fixed point operation on a message graph including nodes with a heaviness based on the weighted terms.