Longitudinal Impact of Preference Biases on Recommender Systems’ Performance
by
Jingjing Zhang
Associate Professor
Fettig/Whirlpool Faculty Fellow
Department of Operations & Decision Technologies
Kelley School of Business
Indiana University
Friday, Dec 3
10:30am – 12 pm | Alter 603
Abstact:
Research studies have shown that recommender systems’ predictions that are observed by users can cause biases in users’ post-consumption preference ratings. Because users’ preference ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the performance of the system in the long run. We use a simulation approach to study the longitudinal impact of preference biases (and their magnitude) on the dynamics of recommender systems’ performance. We look at the influence of preference biases in two conditions: (i) during the normal system use, where biases are typically caused by the system’s inherent prediction errors, and (ii) in the presence of external (deliberate) recommendation perturbations. Our simulation results show that preference biases significantly impair the system’s prediction performance (i.e., prediction accuracy) as well as users’ consumption outcomes (i.e., consumption relevance and diversity) over time. The impact is non-linear to the size of the bias, i.e., large bias causes disproportionately large negative effects. Also, items that are less popular and less distinctive (in terms of their content) are affected more by preference biases. Additionally, intentional recommendation perturbations, even on a small number of items for a short time, substantially amplify the negative impact of preference bias on a system’s longitudinal dynamics and causes long-lasting effects on users’ consumption. Our findings provide important implications for the design of recommender systems.