Feedback-based Approach to Introduce Freshness in Recommendations

TL;DR

This paper proposes a feedback-based method to enhance recommendation freshness by adapting suggestions based on user interactions, including strategies for scenarios with minimal or no user feedback, and introduces a metric for measuring freshness.

Contribution

It introduces a novel feedback loop mechanism for recommender systems to improve freshness, addressing cases with limited user interaction.

Findings

The proposed approach increases recommendation diversity.

The freshness metric effectively quantifies temporal diversity.

The method adapts recommendations based on user feedback.

Abstract

Recommender systems usually face the problem of serving the same recommendations across multiple sessions regardless of whether the user is interested in them or not, thereby reducing their effectiveness. To add freshness to the recommended products, we introduce a feedback loop where the set of recommended products in the current session depend on the user's interaction with the previously recommended sets. We also describe ways of addressing freshness when there is little or even no direct user interaction. We define a metric to quantify freshness by reducing the problem to measuring temporal diversity.