Next-App Prediction by Fusing Semantic Information With Sequential Behavior

Fang, Changjian; Wang, Youquan; Mu, Dejun; Wu, Zhang

doi:10.1109/access.2018.2883377

Cited by 2 publications

(1 citation statement)

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“…The approach gives 83% hit rate for the top 5 apps in each location. Fang et al [15] use a topic model for converting app and users' preferences into latent vectors, and then the KNN algorithm is used. Furthermore, the prediction is built on a chain-augmented Naive Bayes model since the authors use sequential temporal data.…”

Section: Related Workmentioning

confidence: 99%

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

Katsarou

Beierle³

2022

IEEE Access

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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 (userindependent) 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%.INDEX TERMS Human-centered computing, Smartphone, Machine learning algorithms, LSTM.

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Section: Related Workmentioning

confidence: 99%