Recommendation of Mobile Applications based on social and contextual user information

Chamorro-Vela, Dario Fernando; Calvache-Lopez, Pablo Esteban; Corrales, Juan Carlos; Rojas-Potosí, Luis Antonio; Suares, luis Javier; Ordóñez, Hugo; Ordóñez, Armando

doi:10.1016/j.procs.2017.06.090

Cited by 6 publications

(1 citation statement)

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“…The results of this study showed an increase in these models' predictive accuracy. Furthermore, study [38] introduced a recommender system (Vanilla) that considers social and contextual information processes. The system allows the comparison of different recommendation techniques.…”

Section: Application Recommender System Studiesmentioning

confidence: 99%

A Recommender System for Mobile Applications of Google Play Store

Fuad¹,

Bayoumi²,

Al-Yahya³

2020

IJACSA

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With the growth in the smartphone market, many applications can be downloaded by users. Users struggle with the availability of a massive number of mobile applications in the market while finding a suitable application to meet their needs. Indeed, there is a critical demand for personalized application recommendations. To address this problem, we propose a model that seamlessly combines content-based filtering with application profiles. We analyzed the applications available on the Google Play app store to extract the essential features for choosing an app and then used these features to build app profiles. Based on the number of installations, the number of reviews, app size, and category, we developed a content-based recommender system that can suggest some apps for users based on what they have searched for in the application's profile. We tested our model using a k-nearest neighbor algorithm and demonstrated that our system achieved good and reasonable results.

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Section: Application Recommender System Studiesmentioning

confidence: 99%