Enhancing the long-term performance of recommender system
This addresses the long-term performance issue for online commercial systems, though it appears incremental as it builds on existing recommendation methods.
The paper tackles the neglected problem of long-term recommendation accuracy in recommender systems by proposing the Adjustment of Recommendation List (ARL) approach, which significantly enhances long-term accuracy while maintaining healthy item diversity, with an optimal stable parameter identified.
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.