# INSPIRED: Toward Sociable Recommendation Dialog Systems

arXiv: https://arxiv.org/abs/2009.14306 · HTML: https://mlsanity.com/paper/2009.14306/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 30 of 93)

- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation](https://mlsanity.com/paper/2109.08877/) (2021)
- [CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://mlsanity.com/paper/2101.00939/) (2021)
- [Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation](https://mlsanity.com/paper/2112.07924/) (2021)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information](https://mlsanity.com/paper/2112.08140/) (2021)
- [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- [Self-Updatable Large Language Models by Integrating Context into Model Parameters](https://mlsanity.com/paper/2410.00487/) (2024)
- [Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset](https://mlsanity.com/paper/2403.04460/) (2024)
- [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023)
- [KECRS: Towards Knowledge-Enriched Conversational Recommendation System](https://mlsanity.com/paper/2105.08261/) (2021)
- [RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models](https://mlsanity.com/paper/2110.07477/) (2021)
- [Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information](https://mlsanity.com/paper/2506.11680/) (2025)
- [U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](https://mlsanity.com/paper/2305.04774/) (2023)
- [ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders](https://mlsanity.com/paper/2602.16938/) (2026)
- [Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games](https://mlsanity.com/paper/2212.08279/) (2022)
- [Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2404.11773/) (2024)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation](https://mlsanity.com/paper/2405.12119/) (2024)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [Graph Retrieval-Augmented LLM for Conversational Recommendation Systems](https://mlsanity.com/paper/2503.06430/) (2025)
- [Conversational Recommendation: A Grand AI Challenge](https://mlsanity.com/paper/2203.09126/) (2022)
- [Parameter-Efficient Conversational Recommender System as a Language Processing Task](https://mlsanity.com/paper/2401.14194/) (2024)

## Abstract (arXiv)

In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.