计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 210800242-7.doi: 10.11896/jsjkx.210800242
高文斌1, 王睿1, 祖家琛1, 董晨辰2, 胡谷雨1
GAO Wenbin1, WANG Rui1, ZU Jiachen1, DONG Chenchen2, HU Guyu1
摘要: 随着Web服务数量的迅速增长,服务过载的问题逐步显现。为了解决服务过载的问题,帮助用户快速定位高质量服务,服务推荐成为了服务计算领域的研究热点。针对目前服务推荐中冷启动及数据稀疏的难点问题,提出了一种基于多目特征交叉的服务质量(Quality of Service,QoS)预测推荐算法(Service Recommendation Algorithm Based on Multi-features Crossing,SRMFC),通过“词嵌入”方法实现多目特征的引入,提升算法在应对冷启动时的表现;同时,应用神经网络完成多目特征的自动交叉,相比于传统协同过滤(Collaborative Filtering,CF)、因子分解机(Factorization Machine,FM)等方法,该算法能实现特征之间相互关系的深入挖掘,从而提升算法在应对数据极度稀疏场景下的学习能力。在公共数据集上的实验结果表明,基于多目特征交叉的服务推荐算法在不同数据稀疏性场景下,相比于近几年主流的服务推荐算法,服务质量预测误差至少降低20%。
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