Conversational Search System Personalizing URL Documents
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Solution Overview
Problem
Current automated customer service systems, such as chatbots, are inefficient in handling varied customer requests, as they often rely on common needs and fail to provide customized solutions when user needs are specific.
Innovation Solution
A conversational search system that monitors and analyzes user utterances to classify context, intervenes when additional information is needed, and provides resource recommendations using a Documentation Recommendation Module, enabling the construction of customized URLs through API calls, and predicts future resource needs by training on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated customer service systems rely on common needs and standardized responses, then service efficiency is improved, but the ability to address specific user needs deteriorates
Solution Approach 1:
The system performs preliminary classification of user utterances into context categories during the conversation, preparing structured data about user needs before resource recommendation is required. This preliminary analysis enables the system to quickly retrieve and recommend appropriate resources without sacrificing personalization.
Solution Approach 2:
The system continuously monitors and analyzes user utterances during the dialogue, using this feedback to dynamically adjust resource recommendations. By incorporating real-time feedback from user interactions, the system can adapt to specific user needs while maintaining efficient automated service.
2Reliability
If the system provides customized resource recommendations for each user, then user satisfaction is improved, but system complexity increases
Solution Approach 1:
The system segments the resource recommendation process into distinct modules: utterance monitoring, context classification, resource matching, and recommendation generation. This segmentation allows each component to handle a specific aspect of personalization, reducing overall system complexity while maintaining customized recommendations.
Solution Approach 2:
The system introduces context classification as an intermediary layer between user utterances and resource recommendations. This intermediary structure simplifies the matching process by organizing user needs into standardized categories, making resource recommendation more manageable despite the customization requirements.
3Measurement precision
If the system monitors and analyzes all user utterances in real-time, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of user utterances into context categories during the conversation flow, preparing structured data about user needs before resource recommendation is required. This preliminary analysis enables the system to quickly retrieve and recommend appropriate resources without sacrificing personalization.
Solution Approach 2:
The system focuses its analysis on extracting and classifying only the essential features of user utterances relevant to resource recommendation, rather than analyzing every aspect of the conversation in detail. This partial action approach maintains prediction accuracy while reducing processing time.
Data Source
AI summary
A method, computer system, and a computer program for conducting a conversational search. In one embodiment, the method includes monitoring a dialogue involving at least one user and capturing user utterances provided during the dialogue. These user utterances are then analyzed and classified according to the context of the dialogue. The dialogue is intervened upon the determination that a user needs additional information and/or upon execution of an action on behalf of the user and based on the plurality of user utterances and context. The Required information may be provided back to the user using Documentation Recommendation Module. The Documentation Recommendation Module determines a valid resource recommendation as determined by a combination of the context and a resource that includes additional information.