# MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce

arXiv: https://arxiv.org/abs/2304.12636 · HTML: https://mlsanity.com/paper/2304.12636/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 19 of 19)

- [PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants](https://mlsanity.com/paper/2506.09902/) (2025)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue](https://mlsanity.com/paper/2602.03548/) (2026)
- [Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts](https://mlsanity.com/paper/2504.14375/) (2025)
- [Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation](https://mlsanity.com/paper/2604.09698/) (2026)
- [SAGE: A Service Agent Graph-guided Evaluation Benchmark](https://mlsanity.com/paper/2604.09285/) (2026)
- [Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems](https://mlsanity.com/paper/2310.17749/) (2023)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations](https://mlsanity.com/paper/2508.18132/) (2025)
- [Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue](https://mlsanity.com/paper/2602.22697/) (2026)
- [MegaChat: A Synthetic Persian Q&A Dataset for High-Quality Sales Chatbot Evaluation](https://mlsanity.com/paper/2511.23397/) (2025)
- [Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents](https://mlsanity.com/paper/2602.05597/) (2026)
- [SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization](https://mlsanity.com/paper/2604.26394/) (2026)
- [Improving Multi-turn Dialogue Consistency with Self-Recall Thinking](https://mlsanity.com/paper/2605.15102/) (2026)
- [IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search](https://mlsanity.com/paper/2607.01530/) (2026)
- [Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning](https://mlsanity.com/paper/2609.00014/) (2026)

## Abstract (arXiv)

Conversational systems can be particularly effective in supporting complex information seeking scenarios with evolving information needs. Finding the right products on an e-commerce platform is one such scenario, where a conversational agent would need to be able to provide search capabilities over the item catalog, understand and make recommendations based on the user's preferences, and answer a range of questions related to items and their usage. Yet, existing conversational datasets do not fully support the idea of mixing different conversational goals (i.e., search, recommendation, and question answering) and instead focus on a single goal. To address this, we introduce MG-ShopDial: a dataset of conversations mixing different goals in the domain of e-commerce. Specifically, we make the following contributions. First, we develop a coached human-human data collection protocol where each dialogue participant is given a set of instructions, instead of a specific script or answers to choose from. Second, we implement a data collection tool to facilitate the collection of multi-goal conversations via a web chat interface, using the above protocol. Third, we create the MG-ShopDial collection, which contains 64 high-quality dialogues with a total of 2,196 utterances for e-commerce scenarios of varying complexity. The dataset is additionally annotated with both intents and goals on the utterance level. Finally, we present an analysis of this dataset and identify multi-goal conversational patterns.