# Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action arXiv: https://arxiv.org/abs/2504.08754 · HTML: https://mlsanity.com/paper/2504.08754/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. - Builds on 42 of its 62 references that are in the map. - First posted on arXiv 28 Mar 2025. ## Placed in - LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/) ## Builds on (most cited first, 30 of 42) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024) - [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021) - [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024) - [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019) - [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://mlsanity.com/paper/2305.10142/) (2023) - [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018) - [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024) - [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020) - [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019) - [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023) - [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018) - [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020) - [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019) - [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019) - [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022) - [Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents](https://mlsanity.com/paper/2311.00262/) (2023) - [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023) - [Large Language Models Empowered Personalized Web Agents](https://mlsanity.com/paper/2410.17236/) (2024) - [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018) - [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023) - [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023) - [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023) - [Evaluating Conversational Recommender Systems via User Simulation](https://mlsanity.com/paper/2006.08732/) (2020) - [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022) - [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024) - [LLM-Powered User Simulator for Recommender System](https://mlsanity.com/paper/2412.16984/) (2024) - [Improving Dialog Systems for Negotiation with Personality Modeling](https://mlsanity.com/paper/2010.09954/) (2020) - [A Large Language Model Enhanced Conversational Recommender System](https://mlsanity.com/paper/2308.06212/) (2023) ## Built on by (most cited first, 6 of 6) - [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025) - [RPM: Reasoning-Level Personalization for Black-Box Large Language Models](https://mlsanity.com/paper/2505.21082/) (2025) - [Think Thrice Before You Speak: Dual knowledge-enhanced Theory-of-Mind Reasoning for Persuasive Agents](https://mlsanity.com/paper/2605.22602/) (2026) - [Hypotheses-Guided Self Distillation for Continual Personalization](https://mlsanity.com/paper/2609.00251/) (2026) - [Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems](https://mlsanity.com/paper/2605.05250/) (2026) - [RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce](https://mlsanity.com/paper/2609.38974/) (2026) ## Abstract (arXiv) 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.