Feedback-based Approach to Introduce Freshness in Recommendations
This addresses the issue of reduced effectiveness in recommender systems for users due to repetitive recommendations, though it appears incremental in approach.
The paper tackles the problem of stale recommendations in recommender systems by introducing a feedback loop that adjusts recommendations based on user interactions, and it defines a metric to quantify freshness through temporal diversity.
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.