Mobile recommendations based on interest prediction from consumer's installed apps–insights from a large-scale field study

Frey, Remo Manuel; Xu, Ran; Ammendola, Christian; Moling, Omar; Giglio, Giuseppe; Ilic, Alexander

doi:10.1016/j.is.2017.08.006

Cited by 14 publications

(6 citation statements)

References 24 publications

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“…[12] used user behavior logs to calculate user similarity. In addition, [1,5,8,10,16,20,25,30,31,37,39] used the app's own characteristics, such as app review text, app version evolution and other information, to construct an app similarity model and generate a recommendation list for users.…”

Section: Related Workmentioning

confidence: 99%

Incorporating contextual information into personalized mobile applications recommendation

et al. 2021

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With the rise of the mobile internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. App has the functional exclusiveness feature, which means the target users will not reuse apps with the same function in a certain spatiotemporal information. Most of the existing recommended methods for apps ignore the functional exclusiveness feature which makes it difficult to further improve the recommendation performance of the app recommendation. To solve this problem, we aim to improve the app recommendation performance, and propose a Personalized Contextaware Mobile App Recommendation Approach, called PCMARA. PCMARA comprehensively considers the user and app contextual information, which can mine the users app usage preference effectively. Specifically, (1) PCMARA explores the contextual characteristic of app, and constructs the app contextual factors for app which represent the function of app. (2) For the app

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Section: Related Workmentioning

confidence: 99%

Incorporating contextual information into personalized mobile applications recommendation

et al. 2021

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“…The presented solutions for addressing data privacy risks contain procedures to transfer aggregated and non-personal data (Chernbumroong et al 2014;Frey et al 2017;Gkatziaki et al 2017;Satyanarayanan 2017). For instance, Gkatziaki et al (2017) analyzes social media broadcasts to divide a city into smaller subspaces and identifying urban centers.…”

Section: Data Privacymentioning

confidence: 99%

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

Eggert

Alberts

2020

Bus Res

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Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.

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“…Os trabalhos [Frey et al 2017, Cheng et al 2018 utilizam LDA (Latent Dirichlet Allocation) para fazer as recomendações. O primeiro utilizou LDA para selecionar os tópicos principais dentre as descrições de aplicativos, utilizando a probabilidade de o usuário gostar de cada tópico como insumo para um modelo baseado no algoritmo Floresta Aleatória.…”

Section: Trabalhos Relacionadosunclassified

Impacto do uso de informações demográficas para a recomendação de aplicativos móveis

Souza¹,

Santos²,

Silva³

et al. 2020

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2020)

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Nos últimos anos, tem-se percebido um aumento significativo no uso de dispositivos móveis e aplicativos. Considerando o grande número de aplicativos, usuários enfrentam dificuldades em decidir qual instalar. Por outro lado, as provedoras de aplicativos enfrentam dificuldades para selecionar usuários para direcionar campanhas de marketing com o objetivo de aumentar a taxa de conversão. Neste trabalho, é proposta uma solução que faz a recomendação de aplicativos com base em dados de similaridades entre usuários e entre aplicativos, e também em informações demográficas da região de residência do usuário. Foi feita uma avaliação com dados reais de 7.406 usuários, e os resultados mostraram que a solução proposta se sobressaiu em até 14 pontos percentuais de precisão quando comparada com uma solução tradicional que não considera os dados demográficos. Além disso, a potencial taxa de conversão aumentou em até 30 vezes, quando comparada a usuários selecionados aleatoriamente.

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Mobile recommendations based on interest prediction from consumer's installed apps–insights from a large-scale field study

Cited by 14 publications

References 24 publications

Incorporating contextual information into personalized mobile applications recommendation

Incorporating contextual information into personalized mobile applications recommendation

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

Impacto do uso de informações demográficas para a recomendação de aplicativos móveis

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