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首页> 外文期刊>International Journal of Computational Science and Engineering >A network traffic-aware mobile application recommendation system based on network traffic cost consideration
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A network traffic-aware mobile application recommendation system based on network traffic cost consideration

机译：基于网络流量成本考虑的网络流量感知移动应用推荐系统

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摘要

A large amount and different types of mobile applications (or apps) are being offered to end users via app markets. Existing mobile app recommender systems generally recommend the most popular mobile apps to mobile users to facilitate the proper selection of mobile apps. However, these apps normally generate network traffic, which will consume the user's mobile data plan and may even cause potential security issues. Therefore, more and more mobile users are hesitant or even reluctant to use the mobile apps that are recommended by the mobile app markets. To fill this crucial gap, this paper proposes a mobile app recommendation approach which can provide app recommendations by considering both the apps' popularity and their traffic cost. To achieve this goal, this paper first estimates app network traffic score based on bipartite graph. Then, this paper proposes a flexible approach based on benefit-cost analysis, which can recommend apps by maintaining a balance between the apps' popularity and the traffic cost concern. Finally, this paper evaluates our approach with extensive experiments on a largescale data set collected from Google Play. The experimental results clearly validate the effectiveness and efficiency of our approach.
机译：通过App Markets为最终用户提供大量和不同类型的移动应用程序（或应用程序）。现有的移动应用程序推荐系统通常推荐最受欢迎的移动应用程序到移动用户，以方便正确选择移动应用。但是，这些应用程序通常会生成网络流量，这将消耗用户的移动数据计划，甚至可能导致潜在的安全问题。因此，越来越多的移动用户犹豫不决，甚至不愿意使用移动应用市场推荐的移动应用程序。为了填补这种关键差距，本文提出了一种移动应用程序推荐方法，可以通过考虑应用程序的流行率和流量成本来提供应用程序建议。为实现这一目标，本文首先估计基于二分图的应用网络流量分数。然后，本文提出了一种基于惠益成本分析的灵活方法，可以通过维持应用流行度与交通成本关注之间的平衡来推荐应用。最后，本文评估了我们在从Google Play收集的大型数据集上进行了广泛的实验。实验结果明确验证了我们方法的有效性和效率。

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