A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

Zhu, Ke; Xiao, Yingyuan; Zheng, Wenguang; Xu, Jing; Hsu, Ching‐Hsien

doi:10.1109/access.2020.3046654

Cited by 5 publications

(4 citation statements)

References 33 publications

(40 reference statements)

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“…Then the list has been crossreferenced with the predicted preference to generate the recommendation list that includes the apps used by similar users and not considered as candidate apps by end-users. [7].…”

Section: Related Workmentioning

confidence: 99%

CTRS- a Recommendation Scheme for Apps Based on Topic Discoverability and Personality Matching

Jeeva¹,

Muthukumaran²

2022

Preprint

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The recommendation of apps to the users is still a challenging task and differs from time to time. The past activities of the user alone don’t help much in recommendation systems. It includes preferences, trends, location-based info, new arrivals, etc. For that, app features have to be correlated with the user profile to identify the apps for the recommendation. The reviews have been analyzed in two different ways. In the first part, hidden topics in reviews have been extracted that may be the app feature that has not been mentioned explicitly and app features can get updated. In the other part, user personality interests are discovered in the reviews and activities explored by the user with the known attributes like watch history, wish list, liked apps, previously installed apps, and trends in the platform. With CTRS, app attributes are extracted and an app vector has been created after the subsequent steps of filtering and pooling. It will be get updated in regular intervals due to the dynamic behavior of attributes like new features, current trends, rating, ranking, etc. From the derived app vector, the recommendation system decides whether to recommend an app or not when it encounters during the active session of the user. The experiment was conducted on an app store dataset from Kaggle over 2,54,303 apps in different categories, attributes, and reviews with polarity values. After analysis, the success rate of CTRS has been achieved up to 75% over higher and lower ranked apps in charts.

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Section: Related Workmentioning

confidence: 99%

CTRS- a Recommendation Scheme for Apps Based on Topic Discoverability and Personality Matching

Jeeva¹,

Muthukumaran²

2022

Preprint

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“…For this, the authors used Latent Topics to characterize a user's interests and relate them to permission preferences for each application category. The work of Zhu et al (2021) also uses app usage information as well as contextual user mobility data. The authors associate users across dynamic geographic areas, so applications are recommended based on neighboring users in the same region.…”

Section: Related Workmentioning

confidence: 99%

Investigating the impact of demographic and device information in the recommendation of mobile applications

Souza

Figueiredo²,

Silva³

et al. 2022

JISA

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The number of people with access to mobile devices, as well as applications to these devices (i.e., apps), has been increasing significantly. Thus, users have to choose among a large number of apps proposing to do the same functions, those that better serve them. A possible solution to this problem is the adoption of recommendation systems. Meanwhile, usually these systems consider only users' preferences to create a profile or request sensitive data (e.g., call and message logs). This work investigates the impact of using demographic and device information on app recommendation by using only easy-to-obtain data to enrich a user profile. We evaluate two approaches: a similarity-based Collaborative Filtering with a limited number of apps and a topic-based approach (i.e., LDA) with wider large-scale data. We also inspected the results under both apps and categories context. The general results reveal that the enriched data provides a better app recommendation with the addition of information about the user's region mean wage achieving up to 210% (or 12 percentage points) of improvement in terms of recall.

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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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A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

Cited by 5 publications

References 33 publications

CTRS- a Recommendation Scheme for Apps Based on Topic Discoverability and Personality Matching

CTRS- a Recommendation Scheme for Apps Based on Topic Discoverability and Personality Matching

Investigating the impact of demographic and device information in the recommendation of mobile applications

Incorporating contextual information into personalized mobile applications recommendation

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