A Conceptual Framework for Conversational Search and Recommendation: Modeling Agent-Human Interactions During Conversational Search

核心概念

This paper proposes a conceptual framework to model the actions and decisions of users and agents during the conversational search process. The framework outlines the different actions that users and agents can perform, as well as the key decision points the agent needs to navigate to facilitate a successful and satisfactory conversational search experience.

摘要

The paper presents a conceptual framework for modeling the interactions between users and agents during the conversational search process. It first provides background on the capabilities required for conversational agents and the actions and intents identified in prior work.

The core of the framework enumerates the different actions that users and agents can perform during the conversation. On the user side, these include revealing information about their needs, refining or expanding their requirements, inquiring about the available options, navigating the search results, and interrogating the agent's understanding.

On the agent side, the key actions include eliciting information from the user, revealing and summarizing the available options, suggesting alternatives, explaining its reasoning, and managing the flow of the conversation.

The framework also outlines the key decisions the agent needs to make, such as determining the appropriate dialog policy, identifying the user's intent, selecting the best questions to ask, deciding when to elicit more information versus revealing options, and how to best present the available information to the user.

The conceptual framework provides a structured way to think about the conversational search process and the various components involved. It serves as a starting point for more formal modeling, implementation, and empirical evaluation of conversational search agents.

統計資料

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引述

"The conversational search task aims to enable a user to resolve information needs via natural language dialogue with an agent."
"The goal of this paper is to develop a conceptual framework of different actions and intents, along with the key decision points within the conversation."
"Essentially, this paper provides a conceptualization of the conversational search process between an agent and user, which provides a framework and a starting point for research, development and evaluation of conversational search agents."

從以下內容提煉的關鍵洞見

A Conceptual Framework for Conversational Search and Recommendation: Conceptualizing Agent-Human Interactions During the Conversational Search Process

by Leif Azzopar... 於 arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08630.pdf

深入探究

How can this conceptual framework be extended to model more complex conversational search scenarios, such as multi-turn interactions or open-domain search tasks?

To extend this conceptual framework for more complex conversational search scenarios, several enhancements can be considered. Firstly, incorporating a mechanism to track the context of multi-turn interactions is crucial. This involves maintaining a history of user inputs and agent responses to ensure continuity and coherence in the conversation. Additionally, the framework can be expanded to include a wider range of user actions, such as expressing preferences, providing feedback, or changing the topic, to accommodate the dynamic nature of open-domain search tasks. Furthermore, integrating machine learning algorithms for intent recognition and context understanding can enhance the framework's ability to adapt to diverse user queries and interactions.

What are the potential limitations of this framework, and how could it be improved to better capture the nuances of human-agent interactions during conversational search?

One potential limitation of this framework is its static nature, as it may not fully capture the dynamic and evolving nature of human-agent interactions in conversational search. To address this, the framework could be enhanced with a more sophisticated dialogue management system that can handle interruptions, digressions, and context shifts seamlessly. Additionally, incorporating sentiment analysis and emotion recognition capabilities can help the agent better understand and respond to the user's emotional cues during the conversation. Moreover, integrating natural language generation techniques can improve the agent's ability to generate more human-like responses, enhancing the overall conversational experience.

How can the insights from this conceptual framework be applied to the design and development of practical conversational search systems that can be deployed in real-world applications?

The insights from this conceptual framework can be instrumental in guiding the design and development of practical conversational search systems for real-world applications. By leveraging the identified actions, intents, and decision points, developers can create more intuitive and user-friendly conversational interfaces that facilitate effective information retrieval. Implementing a robust dialogue management system based on the framework's principles can enable seamless interactions between users and agents, leading to more engaging and productive search experiences. Furthermore, conducting user studies and iterative testing based on the framework can help refine and optimize the conversational search system for enhanced performance and user satisfaction.