WhatsNextApp: LSTM-based next-app prediction with app usage sequences
| dc.contributor.author | Katsarou, Katerina | |
| dc.contributor.author | Yu, Geunhye | |
| dc.contributor.author | Beierle, Felix | |
| dc.date.accessioned | 2022-05-05T14:19:35Z | |
| dc.date.available | 2022-05-05T14:19:35Z | |
| dc.date.issued | 2022-02-10 | |
| dc.description.abstract | 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%. | en |
| dc.description.sponsorship | DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin | en |
| dc.identifier.eissn | 2169-3536 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/16797 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-15575 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 384 Kommunikation, Telekommunikation | de |
| dc.subject.other | human-centered computing | en |
| dc.subject.other | smartphone | en |
| dc.subject.other | machine learning algorithms | en |
| dc.subject.other | LSTM | en |
| dc.title | WhatsNextApp: LSTM-based next-app prediction with app usage sequences | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1109/ACCESS.2022.3150874 | en |
| dcterms.bibliographicCitation.journaltitle | IEEE access | en |
| dcterms.bibliographicCitation.originalpublishername | IEEE | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 18247 | en |
| dcterms.bibliographicCitation.pagestart | 18233 | en |
| dcterms.bibliographicCitation.volume | 10 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Telekommunikationssysteme::FG Service-centric Networking | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Service-centric Networking | de |
| tub.affiliation.institute | Inst. Telekommunikationssysteme | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |
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