Pointwise prediction and Learning to Rank (L2R) are two hot strategies to model user preference in recommender systems. Currently, these two types of approaches are often considered independently, and most existing efforts utilize them separately. Unfortunately, pointwise prediction tends to cause the problem of overfitting, while L2R is more prone to higher variance. On the other hand, the advantages of multi-task learning and ensemble learning inspire us to utilize multiple approaches jointly so that methods can promote together synergistically. Therefore, we propose a new framework called CPL, where pointwise prediction and L2R are inherently combined to improve the performance of top-N recommendations. To verify the effectiveness of CPL, an instantiation of CPL, which is named CPLmg, is introduced. CPLmg is based on two components, i.e., Factorized SLIM (Sparse LInear Method) and GAPfm (Graded Average Precision factor model), to perform pointwise prediction and L2R, respectively. Different from the original version of SLIM, FSLIM reconstructs a denser representation both for users and items. Moreover, the low-rank users’ and item’s latent factor matrices act as a bridge between FSLIM and GAPfm. Extensive experiments on four real-world datasets show that CPLmg significantly outperforms the compared methods. To explore other possible combinations for CPL further, we selected another pointwise method, i.e., FunkSVD, and L2R approach, i.e., BPR, to implement CPLdb. The experimental results demonstrate the superiority of CPL again as it can help improve the performance of its pointwise prediction and L2R components.

逐点预测和等级学习（L2R）是在推荐器系统中建模用户偏好的两个热门策略。当前，这两种类型的方法通常被独立考虑，并且大多数现有的努力都分别使用它们。不幸的是，逐点预测往往会导致过度拟合的问题，而L2R更容易出现较高的方差。另一方面，多任务学习和整体学习的优势促使我们共同使用多种方法，从而使方法可以协同促进。因此，我们提出了一个称为CPL的新框架，该框架固有地结合了逐点预测和L2R来提高top-N建议的性能。为了验证CPL的有效性，引入了名为CPLmg的CPL实例。CPLmg基于两个组成部分，即 分解SLIM（稀疏线性方法）和GAPfm（分级平均精度因子模型）分别执行逐点预测和L2R。与SLIM的原始版本不同，FSLIM为用户和项目重建了更密集的表示形式。此外，低等级用户和项目的潜在因子矩阵充当FSLIM和GAPfm之间的桥梁。在四个真实世界的数据集上进行的广泛实验表明，CPLmg明显优于所比较的方法。为了进一步探索CPL的其他可能组合，我们选择了另一种逐点方法（即FunkSVD）和L2R方法（即BPR）来实现CPLdb。实验结果再次证明了CPL的优越性，因为它可以帮助改善其逐点预测和L2R组件的性能。