# Preference Elicitation with Soft Attributes in Interactive Recommendation

arXiv: https://arxiv.org/abs/2311.02085 · HTML: https://mlsanity.com/paper/2311.02085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [Thompson Sampling for Complex Bandit Problems](https://mlsanity.com/paper/1311.0466/) (2013)
- [ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes](https://mlsanity.com/paper/2011.04812/) (2020)
- [On Interpretation and Measurement of Soft Attributes for Recommendation](https://mlsanity.com/paper/2105.09179/) (2021)
- [Gradient-based Optimization for Bayesian Preference Elicitation](https://mlsanity.com/paper/1911.09153/) (2019)

## Built on by (most cited first, 3 of 3)

- [Batch Active Learning of Reward Functions from Human Preferences](https://mlsanity.com/paper/2402.15757/) (2024)
- [Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree](https://mlsanity.com/paper/2410.12217/) (2024)
- [Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)](https://mlsanity.com/paper/2510.02331/) (2025)

## Abstract (arXiv)

Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred items from a slate, or attribute queries that ask them to express their preferences for item characteristics. Unfortunately, users often wish to describe their preferences using soft attributes for which no ground-truth semantics is given. Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation. Our techniques query users using both items and soft attributes to update the recommender system's belief about their preferences to improve recommendation quality. We demonstrate the effectiveness of our methods vis-a-vis competing approaches on both synthetic and real-world datasets.