Human Variability and the Explore-Exploit Trade-Off in Recommendation.

Area of Science:

• Human-Computer Interaction
• Artificial Intelligence
• Information Retrieval

Background:

• The internet's vastness requires algorithms to help users navigate information and products.
• Recommender systems face a critical challenge: balancing exploration (discovering new items) and exploitation (recommending known high-rated items).
• This exploration-exploitation trade-off is influenced by human variability within the user-algorithm interaction loop.

Purpose of the Study:

• To characterize the exploration-exploitation trade-off in recommender systems as a function of human variability.
• To develop a unifying model bridging active learning and information recommendation.
• To analyze how human variability impacts the effectiveness of recommender system algorithms.

Main Methods:

• Introduced a unifying model to create a continuum of algorithms along the exploration-exploitation spectrum.
• Conducted two experiments measuring trade-off behavior across different levels of human variability.
• Performed a comprehensive simulation study systematically varying human variability.

Main Results:

• The severity of the exploration-exploitation trade-off increases with greater human variability.
• A specific regime of low human variability was identified where balanced algorithms can effectively manage the trade-off.
• The study provides a quantitative understanding of the relationship between human variability and recommender system performance.

Conclusions:

• Human variability is a key factor determining the challenge of the exploration-exploitation trade-off in recommender systems.
• Algorithms can overcome this trade-off in low-variability environments by balancing exploration and exploitation.
• Future research should consider human variability to design more effective and personalized recommender systems.