面向服务推荐的QoS成列协同排序算法

QoS-aware listwise collaborative ranking algorithm for service recommendation

针对传统基于服务质量(QoS)预测的推荐方法较少考虑服务间的排序对产生推荐列表的影响,不能准确体现用户偏好的问题。本文提出了一种基于QoS排序学习的服务推荐算法,选用计算复杂度较低的成列损失函数来优化矩阵因式分解模型,并通过挖掘用户间的近邻信息来进一步提高QoS排序的准确性。在真实数据集上的大量实验表明,该算法具有良好的性能。

With the increasing number of candidate services that meet the same function on the Internet, service selection becomes more and more difficult, and service recommendation becomes the key issue that needs to be solved urgently. However, the traditional service QoS prediction based recommendation method pays less attention to the role of the service ranking to the recommendation list, which can not accurately reflect the user preference. To solve the above problems, this paper proposes a QoS ranking learning based service recommendation algorithm. It selects low computational complexity listwise loss function to optimize the matrix factorization model, and further improves the accuracy of QoS ranking by mining the neighbor information between users. Experiments on real datasets show that the proposed algorithm has good performance.