Conversational Recommendation Timing Using Discourse Trees
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Solution Overview
Problem
Existing computer-implemented systems fail to fully analyze utterances in conversations between user devices and autonomous agents, leading to ineffective and intrusive recommendation systems that do not consistently provide relevant and timely recommendations.
Innovation Solution
Utilizing communicative discourse trees and machine learning to analyze user sentiment and intent, generating appropriate recommendations that are seamlessly integrated into conversations based on changes in sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used, then recommendations can be provided to users, but the analysis of utterances is insufficient and recommendations become intrusive
Solution Approach 1:
The system performs preliminary sentiment analysis on user utterances before generating recommendations. By analyzing sentiment scores of consecutive utterances and detecting changes exceeding a threshold, the system determines the appropriate timing for recommendation insertion, ensuring recommendations are provided only when user sentiment indicates receptiveness, thereby reducing intrusion while maintaining analysis accuracy
Solution Approach 2:
The system continuously monitors sentiment scores from user utterances and uses this feedback to dynamically adjust recommendation timing. The sentiment analysis feedback loop allows the system to detect when user sentiment changes indicate readiness for recommendations, creating a responsive mechanism that balances thorough analysis with minimal user intrusion
2Reliability
If comprehensive utterance analysis is performed, then recommendation relevance improves, but system complexity increases
Solution Approach 1:
The system segments utterance analysis into distinct functional components: sentiment score determination, sentiment change detection, threshold comparison, and recommendation timing decision. This segmentation allows each component to be independently optimized and managed, maintaining high recommendation relevance through comprehensive analysis while controlling overall system complexity through modular architecture
Solution Approach 2:
The sentiment analysis system serves multiple functions: it evaluates utterance quality, determines recommendation timing, and monitors conversation flow. This multi-functionality allows a single analytical mechanism to drive multiple aspects of recommendation delivery, improving reliability across different functions without proportionally increasing system complexity
3Productivity
If recommendations are inserted frequently, then user needs are addressed promptly, but user experience deteriorates due to intrusion
Solution Approach 1:
The system uses sentiment score feedback from consecutive utterances to dynamically control recommendation insertion frequency. By monitoring whether sentiment changes exceed a predefined threshold, the system automatically determines when recommendations are appropriate, ensuring prompt response to user needs while preventing excessive intrusion that would degrade user experience
Solution Approach 2:
The recommendation insertion mechanism is made dynamic by continuously adjusting based on real-time sentiment analysis. The system adapts its recommendation frequency to match user sentiment states, inserting recommendations promptly when sentiment indicates readiness while reducing or eliminating recommendations when sentiment suggests the user is not receptive, thereby balancing productivity with user experience
Data Source
AI summary
Techniques are disclosed for improved autonomous agents that can provide a recommendation in a non-intrusive, conversational manner. In an aspect, a method determines a first sentiment score for a first utterance and a second sentiment score for a second utterance, each sentiment score indicating an emotion indicated by the respective utterance. The method further identifies that a difference between the first sentiment score and the second sentiment score is greater than a threshold. The method further extracts a noun phrase from the second utterance. The method identifies a text fragment that includes an entity that corresponds to the noun phrase. The method identifies that the text fragment addresses a claim of the second utterance. The method forms a third utterance that includes the a recommendation related to the second utterance and adds the third utterance to the sequence of utterances after the second utterance.