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An app usage recommender system: improving prediction accuracy for both warm and cold start users

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Abstract

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.

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Authors and Affiliations

  1. Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau, China

    Di Han, Jianqing Li, Ruibin Liu & Hai Chen

  2. Computer and Information Engineering College, Guizhou University of Commerce, Guiyang, China

    Wenting Li

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  1. Di Han
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  2. Jianqing Li
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  3. Wenting Li
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  5. Hai Chen
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Correspondence to Jianqing Li.

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Han, D., Li, J., Li, W. et al. An app usage recommender system: improving prediction accuracy for both warm and cold start users. Multimedia Systems 25, 603–616 (2019). https://doi.org/10.1007/s00530-018-0601-1

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