It is becoming increasingly difficult to find a particular app on a smartphone due to the increasing number of apps installed. Consequently, it is important to be able to quickly and accurately predict the next app to be used. Two problems arise in predicting next-app usage from the app usage history. One is that some algorithms do not consider the increasing amount of training data available over time, which causes the prediction accuracy to decrease over time. The other is that although some algorithms do consider the aggregation of training data over time, they rebuild their models using all historical data once the amount of new data has reached a certain limit, thus greatly increasing the remodeling time. To reduce the remodeling time, we utilize an modified incremental k-nearest neighbours (IkNN) algorithm to implement a recommender system called Predictor. When the IkNN is used for predicting next-app usage, a new problem is found. When modeling the training data, the classification accuracy decreases as the number of app features increases. After studying the relationships among the contextual features of apps, we design a cluster effective value (CEV), which can compensate for the error induced by multidimensional features, to improve the classification accuracy. It is shown that the IkNN algorithm with the CEV achieves a higher and more stable prediction accuracy compared with that of the algorithm without the CEV. Furthermore, we proposed a the Cold Start strategy: an efficient dynamic collaborative filtering fusion algorithm that provides app Cold Start prediction. Large-scale experiments show that Predictor offers a reduced remodeling time and an improved prediction accuracy.

中文翻译：

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应用使用推荐系统：提高热启动和冷启动用户的预测准确性

由于安装的应用程序数量不断增加，在智能手机上找到特定应用程序变得越来越困难。因此，能够快速准确地预测下一个要使用的应用程序非常重要。从应用程序使用历史预测下一个应用程序使用情况会出现两个问题。一是一些算法没有考虑随着时间的推移可用的训练数据量不断增加，这会导致预测精度随着时间的推移而下降。另一个是，虽然有些算法确实考虑了训练数据随时间的聚合，但一旦新数据量达到一定限度，它们就会使用所有历史数据重建模型，从而大大增加了重构时间。为了减少改造时间，我们利用改进的增量 k 近邻 (IkNN) 算法来实现称为预测器的推荐系统。当 IkNN 用于预测下一个应用程序使用时，发现了一个新问题。在对训练数据建模时，分类准确度会随着应用功能数量的增加而降低。在研究了应用程序上下文特征之间的关系后，我们设计了一个集群有效值（CEV），它可以补偿多维特征引起的误差，以提高分类精度。结果表明，与没有CEV的算法相比，有CEV的IkNN算法实现了更高、更稳定的预测精度。此外，我们提出了冷启动策略：一种高效的动态协同过滤融合算法，可提供应用程序冷启动预测。