Recommender system for mobile users

Ren, Chao; Chen, Jian; Kuo, Yong-Hong; Wu, Di; Yang, Mengqi

doi:10.1007/s11042-017-4527-y

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…For example, the ratings of the user's previously purchased items can be exploited. Recommendation systems can be classified into three major categories to generate a list of recommendations based on a particular prediction technique [8], [9].…”

Section: Recommender Systemsmentioning

confidence: 99%

“…The study [8] discussed assisting the users in choosing the appropriate application using recommendations. The author proposed a recommender system for mobile applications by integrating two methods: tracking user behavior to get his preferences to find new and similar apps to their used ones and utilizing the user's context in order to provide him with useful recommendations by using the Google Play Engine.…”

Section: Application Recommender System Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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: Recommender Systemsmentioning

confidence: 99%

Section: Application Recommender System Studiesmentioning

confidence: 99%