A Next Application Prediction Service Using the BaranC Framework

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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“…Prior works employ Markov models in their approaches [8]- [10]. The authors use app transitions or app sequences for their prediction models.…”

Section: Related Workmentioning

confidence: 99%

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

Katsarou

Beierle³

2022

IEEE Access

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Towards Psychometrics-Based Friend Recommendations in Social Networking Services

Beierle

Grunert

Göndör

et al. 2017

2017 IEEE International Conference on AI &Amp; Mobile Services (AIMS)

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Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. In this paper, we argue that psychometrics, the field of measuring personality traits, can help make meaningful friend recommendations based on an extended social profile containing collected smartphone sensor data. This will support the development of highly distributed Social Networking Services without central knowledge of the social graph.

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A Next Application Prediction Service Using the BaranC Framework

Cited by 3 publications

References 14 publications

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

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

Related Work

Towards Psychometrics-Based Friend Recommendations in Social Networking Services

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