Conversational Agent for Search Intent Refinement
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Solution Overview
Problem
Conventional search engines face limitations in efficiently assisting users in finding desired search results, particularly in domains like stock image searching, where users struggle to express context and intent due to the reliance on keyword-based queries and faceted search systems, leading to cumbersome and time-consuming processes.
Innovation Solution
A conversational search system utilizing a reinforcement learning (RL) agent trained with a user model generated from search engine session logs, allowing for human-like interactions and improved intent understanding through natural language processing and contextual refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional keyword-based search is used, then search engine can process queries systematically, but users struggle to express context and intent effectively
Solution Approach 1:
The patent introduces a conversational agent as an intermediary between the user and the search engine. This agent engages users in natural language conversation to elicit and understand their search intent, then translates this understanding into effective search queries. The conversational agent acts as a mediator that bridges the gap between users' abstract ideas and the structured query requirements of search engines.
2Measurement precision
If users continuously modify search queries to find desired results, then search accuracy may improve, but the process becomes extremely time consuming
Solution Approach 1:
The conversational agent performs preliminary actions by engaging users in conversation before executing the search. Through natural language interaction, the agent proactively gathers information about user intent, context, and preferences, formulating a well-refined search query in advance. This preliminary conversational phase prevents the need for multiple iterative query modifications during the search process.
Solution Approach 2:
The system implements feedback mechanisms where the conversational agent continuously monitors user responses and adjusts its understanding of search intent in real-time. Based on user feedback during the conversation, the agent refines its query formulation dynamically, ensuring that the final search query accurately reflects user needs without requiring multiple separate search attempts.
3Productivity
If broad and abstract search ideas are used initially, then users can start searching quickly, but it becomes difficult to refine search queries to match specific needs
Solution Approach 1:
The conversational agent implements a dynamic approach to query refinement. It begins with broad search concepts based on initial user input and adaptively adjusts the specificity and focus of the search query through ongoing conversation. The agent dynamically incorporates user feedback, preferences, and contextual information to evolve the search query from abstract to specific, maintaining both quick initiation and effective refinement capabilities.
Data Source
AI summary
A conversational agent facilitates conversational searches for users. The conversational agent is a reinforcement learning (RL) agent trained using a user model generated from existing session logs from a search engine. The user model is generated from the session logs by mapping entries from the session logs to user actions understandable by the RL agent and computing conditional probabilities of user actions occurring given previous user actions in the session logs. The RL agent is trained by conducting conversations with the user model in which the RL agent selects agent actions in response to user actions sampled using the conditional probabilities from the user model.