Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action (2025)

Abstract: Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To capture this complexity, we introduce Conversational Sales (CSALES), a novel task that integrates preference elicitation, recommendation, and persuasion within a unified conversational framework. To support realistic and systematic evaluation, we present CSUSER, an evaluation protocol with LLM-based user simulator grounded in real-world behavioral data by modeling fine-grained user profiles for personalized interaction. We also propose CSI, a conversational sales agent that proactively infers contextual user profiles and strategically selects actions through conversation. Comprehensive experiments show that CSI significantly improves both recommendation success and persuasive effectiveness across diverse user profiles.

## introduction conversational recommender systems (crss) aim to engage users in dialogue to better understand their preferences and provide personalized recommendations @xcite @xcite . with advancements in large language models (llms), research has increasingly focused on llm-based crss @xcite , leveraging their reasoning capabilities. through multi-turn interactions, these systems should capture both explicitly stated user preferences and implicit needs from context to enhance recommendation quality @xcite . ## csuser: user simulators for csales overview. we introduce csuser, a user simulation framework consisting of simulators grounded in real-world user interaction histories. each simulator is modeled with a rich user profile composed of fine-grained behavioral components, capturing both long-term preferences and short-term intent. to support evaluation in csales, we also propose a quantitative metric that measures persuasive effectiveness by assessing how often users accept items beyond their initial expected price range. ## conversational sales agent with contextual profiling in this section, we present csi, a conversational sales agent designed to strategically guide users toward purchase decisions in csales. csi dynamically infers a user profile throughout the conversation to select actions that aligned with both long-term preferences and immediate needs. figure 3 illustrates an overview of csi 's process within a single dialogue turn. the core of csi is a contextual user profile pt, which is continuously updated based on the user's latest utterance u t and the preceding conversation history c t-1 = {u 1 , s 1 , . . . , s t-1 }. this profile informs the csi by guiding the selection of the next action a @xmath0, item recommendation, and persuasive explanation. at last, it conditions the generation of the system response s t . by reasoning over this evolving profile, csi adaptively responds to user input, uncovers implicit needs, and delivers strategically personalized recommendations. ## conclusion we present csales, a novel task that integrates preference elicitation, recommendation, and persuasion to better reflect the complexity of realworld e-commerce interactions. to support realistic evaluation, we introduce csuser, modeling diverse user profiles with fine-grained components. we also propose csi, that dynamically infers contextual user profiles to adapt its actions and persuasion strategies to individual behaviors. through comprehensive experiments and human evaluations, we demonstrate the effectiveness of csi and validate the reliability of csuser. our findings highlight the importance of personalized interaction and realistic simulation in advancing crs research. future directions include enhancing long-term strategic planning and expanding simulation capabilities to broader domains.