# A Survey on Intent-aware Recommender Systems

arXiv: https://arxiv.org/abs/2406.16350 · HTML: https://mlsanity.com/paper/2406.16350/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2024.

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

- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval](https://mlsanity.com/paper/2504.20624/) (2025)
- [Where to Explore: A Reach and Cost-Aware Approach for Unbiased Data Collection in Recommender Systems](https://mlsanity.com/paper/2512.14733/) (2025)
- [Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation](https://mlsanity.com/paper/2609.29609/) (2026)

## Abstract (arXiv)

Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users' probable intent of using the service at a certain point in time into account. In recent years, researchers have thus started to address this challenge by incorporating intent-awareness into recommender systems. Correspondingly, a number of technical approaches were put forward, including diversification techniques, intent prediction models or latent intent modeling approaches. In this paper, we survey and categorize existing approaches to building the next generation of Intent-Aware Recommender Systems (IARS). Based on an analysis of current evaluation practices, we outline open gaps and possible future directions in this area, which in particular include the consideration of additional interaction signals and contextual information to further improve the effectiveness of such systems.