  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).
