# Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2308.08496 · HTML: https://mlsanity.com/paper/2308.08496/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 185 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Intent Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2202.02519/) (2022)
- [Discovering New Intents with Deep Aligned Clustering](https://mlsanity.com/paper/2012.08987/) (2020)
- [Fair Decision Making using Privacy-Protected Data](https://mlsanity.com/paper/1905.12744/) (2019)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [An Empirical Analysis of Backward Compatibility in Machine Learning Systems](https://mlsanity.com/paper/2008.04572/) (2020)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation](https://mlsanity.com/paper/2007.15356/) (2020)
- [Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application](https://mlsanity.com/paper/1803.00710/) (2018)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [Facial Expression Analysis under Partial Occlusion: A Survey](https://mlsanity.com/paper/1802.08784/) (2018)
- [The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review](https://mlsanity.com/paper/1511.05263/) (2015)
- [Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search](https://mlsanity.com/paper/2006.07548/) (2020)
- [User Intent Prediction in Information-seeking Conversations](https://mlsanity.com/paper/1901.03489/) (2019)
- [Leveraging Historical Interaction Data for Improving Conversational Recommender System](https://mlsanity.com/paper/2008.08247/) (2020)
- [Session-aware Recommendation: A Surprising Quest for the State-of-the-art](https://mlsanity.com/paper/2011.03424/) (2020)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation](https://mlsanity.com/paper/2202.02576/) (2022)
- [A Context-Aware User-Item Representation Learning for Item Recommendation](https://mlsanity.com/paper/1712.02342/) (2017)
- [Predicting the Objective and Priority of Issue Reports in Software Repositories](https://mlsanity.com/paper/2012.10951/) (2020)
- [Modeling User Behavior with Graph Convolution for Personalized Product Search](https://mlsanity.com/paper/2202.06081/) (2022)

## Built on by (most cited first, 4 of 4)

- [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025)
- [Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach](https://mlsanity.com/paper/2508.05693/) (2025)
- [Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering](https://mlsanity.com/paper/2512.11984/) (2025)
- [A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems](https://mlsanity.com/paper/2604.21030/) (2026)

## Abstract (arXiv)

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.