The Preference Overestimation issue in RL-based recommender systems can be further addressed through several additional strategies:
Exploration Strategies: Implementing more sophisticated exploration strategies, such as epsilon-greedy with a decaying exploration rate or using techniques like Upper Conﬁdence Bound (UCB) to balance exploration and exploitation, can help mitigate the issue of overestimation by encouraging the model to explore a wider range of actions.
Reward Shaping: Introducing reward shaping techniques, where additional rewards are provided to guide the learning process towards desired behaviors, can help in shaping more accurate preferences and reducing overestimation.
Ensemble Learning: Utilizing ensemble learning methods, where multiple models are trained and their predictions are aggregated, can help in reducing the impact of overestimation by considering a diverse set of predictions.
Regularization Techniques: Incorporating regularization techniques, such as L1 or L2 regularization, dropout, or batch normalization, can help prevent the model from overfitting to noisy or outlier data, which can contribute to preference overestimation.
Dynamic Negative Sampling: Implementing dynamic negative sampling strategies, where the number of negative samples used during training is adjusted based on the model's performance or the complexity of the dataset, can help in improving the accuracy of the learned preferences.
Transfer Learning: Leveraging transfer learning approaches, where pre-trained models or knowledge from related tasks are utilized to initialize the recommender system, can provide a more stable starting point and potentially reduce the impact of preference overestimation.