# Enhancing the long-term performance of recommender system

## TL;DR

This paper introduces ARL, a novel method to improve long-term recommendation accuracy in recommender systems by balancing diversity and user preferences, validated through a network evolution model.

## Contribution

The paper proposes the ARL approach to enhance long-term recommendation performance and demonstrates its robustness and effectiveness through simulation.

## Key findings

- Long-term recommendation accuracy is significantly improved.
- Diversity of items in the system is maintained.
- An optimal parameter n* balances diversity and user preferences.

## Abstract

Recommender system is a critically important tool in online commercial system and provide users with personalized recommendation on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user's behaviour. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of item in online system maintains healthy. Notably, an optimal parameter n* of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user's preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n* is stable during evolving network, which reveals the robustness of ARL method.