根据用户上下文动态选择最佳推荐算法，提升个性化推荐精度。
Context-aware adaptive personalised recommendation: a meta-hybrid
- 构建元混合推荐模型，基于上下文与偏好自动选择最优算法。
- 在MovieLens和TMDB数据集上，推荐精度提升20-50%（NDCG/RMSE）。
- 适合需要高个性化推荐的电商平台或内容系统使用。
推荐系统广泛应用于电子商务平台，缓解信息过载问题。传统方法通常采用单一推荐算法，但用户差异显著，‘一刀切’策略效果不佳。本文提出一种上下文感知的自适应个性化推荐框架——元混合推荐模型，利用机器学习预测每个用户会话中表现最优的推荐算法，实现动态选择。该选择基于用户上下文与偏好信息。在MovieLens和The Movie DB数据集上的离线评估表明，所提模型可有效预测最精准推荐算法。理论性能对比显示，其在归一化折扣累积收益（NDCG）与均方根误差（RMSE）指标上较独立推荐方法提升20%-50%。然而，在仅依赖常见用户基础信息时，难以达到最优性能。
原文摘要 · Abstract (English)
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.
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