Considering user dynamic preferences for mitigating negative effects of long-tail in recommender systems

Abstract

Recommender systems focusing solely on accuracy, defined as the similarity of items to users' interests, often encounter the long-tail problem. This issue arises because short-head items, having received more ratings, dominate users' recommendation lists, while long-tail items, which are less popular, are underrepresented to maintain recommendation list precision. Unlike other approaches that treat users' preferences as fixed, this work advocates for considering dynamic user preferences to address the long-tail problem effectively. Specifically, we demonstrate two key observations: 1) users register varying proportions of ratings for long-tail and short-head items over time, and 2) item popularity is dynamic and undergoes changes over time. Consequently, recommendation lists can be dynamically adjusted to include different proportions of popular and unpopular items. We propose adapting the recommendation lists based on users' tenure in the system and their accrued ratings, allowing for higher inclusion of long-tail items for users with longer membership and more registered ratings. Additionally, we maintain an updated list of popular items as their popularity can fluctuate over time. In this study, modifications are made to the memetic algorithm to leverage users' dynamic preferences, demonstrating notable improvements. The proposed method achieves a precision of 90%, surpassing related works by 7% in addressing the long-tail problem, leading to increased participation of unpopular items in recommendation lists.