WhatsNextApp: LSTM-Based Next-App Prediction With App Usage Sequences

IEEE Access

Katerina Katsarou¹  Felix Beierle¹  Geunhye Yu² 

[1] Service-Centric Networking, Technische Universit&x00E4;t Berlin, Berlin, Germany;

关键词: Human-centered computing;    smartphone;    machine learning algorithms;    LSTM;   

DOI  :  10.1109/ACCESS.2022.3150874

来源: DOAJ

【 摘 要 】

Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently registered to the smartphone app system and there is not enough information about his/her preferences and his/her history of smartphone usage. In this work, we try to address the above issues. We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs. Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words. We collect a real-life data set consisting of 975 Android users with over 22 million app usage events. We build a generic (user-independent) WhatsNextApp model and the evaluation with our data set shows that it outperforms related studies for existing users where we achieve a recall@8 (recall for the top 8 apps) of 92%. For the user cold-start problem with the 500 most frequent apps, we achieve a recall@8 of 82.7%.

【 授权许可】

Unknown