Long-Term Recommendation Model for Online Education Systems: A Deep Reinforcement Learning Approach

Abstract

Intelligent tutoring systems serve as tools capable of providing personalized learning experiences, with their efficacy significantly contingent upon the performance of recommendation models. For long-term instructional plans, these systems necessitate the provision of highly accurate, enduring recommendations. However, numerous existing recommendation models adopt a static perspective, disregarding the sequential decision-making nature of recommendations, rendering them often incapable of adapting to novel contexts. While some recent studies have delved into sequential recommendations, their emphasis predominantly centers on short-term predictions, neglecting the objectives of long-term recommendations. To surmount these challenges, this paper introduces a novel recommendation approach based on deep reinforcement learning. We conceptualize the recommendation process as a Markov Decision Process, employing recurrent neural networks to simulate the interaction between the recommender system and the students. Test results demonstrate that our model not only significantly surpasses traditional Top-N methods in hit rate and NDCG concerning the enhancement of long-term recommendations but also adeptly addresses scenarios involving cold starts. Thus, this model presents a new avenue for enhancing the performance of intelligent tutoring systems.