
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.


coremsg

a-conceptual-framework-for-conversational-search-and-recommendation-modeling-agent-human-interactions-during-conversational-search


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


title_rewrite


IterCQR iteratively trains a conversational query reformulation model without human-annotated rewrites, utilizing retrieval signals as a reward to generate queries optimized for off-the-shelf retrievers.


iterative-conversational-query-reformulation-with-retrieval-guidance


Iterative Conversational Query Reformulation with Retrieval Guidance
