Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system to make predictions. However, users vary from each other; thus, a one-fits-all approach seems to be sub-optimal. In this paper, we propose a meta-hybrid recommender that uses machine learning to predict an optimal algorithm. In this way, the best-performing recommender is used for each specific session and user. This selection depends on contextual and preferential information collected about the user. We use standard MovieLens and The Movie DB datasets for offline evaluation. We show that based on the proposed model, it is possible to predict which recommender will provide the most precise recommendations to a user. The theoretical performance of our meta-hybrid outperforms separate approaches by 20-50% in normalized Discounted Gain and Root Mean Square Error metrics. However, it is hard to obtain the optimal performance based on widely-used standard information stored about users.

翻译：推荐系统广泛应用于电子商务系统中，以缓解信息过载问题。最常见的做法是为系统选择一种推荐算法进行预测。然而，用户之间存在差异，因此“一刀切”的方法往往并非最优。本文提出一种元混合推荐系统，利用机器学习预测最优算法。通过这种方式，系统能够为每个特定会话和用户选择性能最佳的推荐器。该选择取决于收集到的用户上下文信息与偏好信息。我们使用标准的MovieLens和The Movie DB数据集进行离线评估。实验表明，基于所提出的模型，能够预测哪种推荐器能为用户提供最精准的推荐。在归一化折损累积增益和均方根误差指标上，我们的元混合方法理论性能优于独立方法20-50%。然而，基于广泛使用的标准用户存储信息难以实现最优性能。