Intelligent Service Recommendation for Cold-Start Problems in Edge Computing
Intelligent Service Recommendation for Cold-Start Problems in Edge Computing
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DOI：
10.1109/access.2019.2909843
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发表时间：
2019-04
期刊：
影响因子：
3.9
通讯作者：
Yichao Zhou;Zhenmin Tang;Lianyong Qi;Xuyun Zhang;Wanchun Dou;Shaohua Wan
中科院分区：
文献类型：
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作者：
Yichao Zhou;Zhenmin Tang;Lianyong Qi;Xuyun Zhang;Wanchun Dou;Shaohua Wan
基于记忆的协同过滤（即，MCF）被认为是向目标用户推荐合适服务的有效技术。然而，如果边缘环境中的推荐数据非常稀疏，则传统的基于MCF的推荐方法可能无法输出任何推荐项目（或服务），即，出现冷启动推荐问题。科普这种冷启动问题，本文提出了一种智能推荐方法Inverse_CF_Rec.具体来说，对于目标用户，我们首先搜索他/她的对手用户（以下统称为“敌人”），然后根据社会平衡理论间接推断出目标用户可能的朋友;最后，根据推导出的目标用户的可能好友，向目标用户推荐最佳服务。在真实数据集WS-DREAM上进行了实验，以验证我们的建议的有效性和效率。实验结果表明，我们的推荐方法在推荐精度和效率方面的优势。
Memory-based collaborative filtering (i.e., MCF) is regarded as an effective technique to recommend appropriate services to target users. However, if recommendation data are very sparse in the edge environment, traditional MCF-based recommendation methods probably cannot output any recommended item (or service), i.e., a cold-start recommendation problem occurs. To cope with this cold-start problem, we propose an intelligent recommendation method named Inverse_CF_Rec. Concretely, for a target user, we first search for his/her opposite users (together referred to as “enemy” hereafter); afterward, we infer the possible friends of the target user indirectly according to Social Balance Theory; finally, optimal services are recommended to the target user based on the derived possible friends of the target user. The experiments are conducted on a real-world dataset WS-DREAM to validate the effectiveness and efficiency of our proposal. The experiment results show the advantages of our recommendation method in terms of recommendation accuracy and efficiency.