# Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks

arXiv: https://arxiv.org/abs/2301.08144 · HTML: https://mlsanity.com/paper/2301.08144/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 88 references that are in the map.
- First posted on arXiv 17 Jan 2023.

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

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction](https://mlsanity.com/paper/1905.11900/) (2019)
- [Coordination with Humans via Strategy Matching](https://mlsanity.com/paper/2210.15099/) (2022)
- [Explanation as a Defense of Recommendation](https://mlsanity.com/paper/2101.09656/) (2021)
- [Towards User-Centric Guidelines for Chatbot Conversational Design](https://mlsanity.com/paper/2301.06474/) (2023)
- [Recommender Systems and Algorithmic Hate](https://mlsanity.com/paper/2209.02159/) (2022)
- [Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing](https://mlsanity.com/paper/1909.03847/) (2019)

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

- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback](https://mlsanity.com/paper/2502.15027/) (2025)
- [Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search](https://mlsanity.com/paper/2510.08992/) (2025)
- [Leveraging Generative AI for Human Understanding: Meta-Requirements and Design Principles for Explanatory AI as a new Paradigm](https://mlsanity.com/paper/2508.06352/) (2025)

## Abstract (arXiv)

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (p < 0.01). Finally, we report an interesting relationship between usability, alignment and perceived intelligence wherein greater perceived alignment of recommendations with one's own preferences leads to higher perceived intelligence (p < 0.01) and higher usability (p < 0.01).