Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Liao, Zhung Xun; Peng, Wen Chih; Yu, Philip S.

doi:10.1007/978-3-642-37453-1_28

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Another approach is to utilise usage behaviour in apps to impact temporal recommendations, as e.g. suggested by Zhung-Xun et al [53]. In contrast to our approach, the authors investigate individual usage traces to build up personalised recommendations.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Usage to Predict Instantaneous App Popularity

et al. 2019

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Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app life-cycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213, 667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps). Less than 0.1% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 6% continuously rise in popularity (Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.

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Section: Related Workmentioning

confidence: 99%

Exploiting Usage to Predict Instantaneous App Popularity

et al. 2019

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“…Related work by Liao et al [14,15] predicts users' preferences by exploring their usage traces of apps on mobile devices. The usage trace in this work records the number of uses of a particular app.…”

Section: Related Workmentioning

confidence: 99%

NudgeAlong: A Case Based Approach to Changing User Behaviour

O’Shea¹,

Delany²,

Lane³

et al. 2014

Case-Based Reasoning Research and Development

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Abstract. Companies want to change the way that users interact with their services. One of the main ways to do this is through messaging. It is well known that different users are likely to respond to different types of messages. Targeting the right message type at the right user is key to achieving successful behaviour change. This paper frames this as a case based reasoning problem. The case representation captures a summary of a user's interactions with a company's services over time. The case solution represents a message type that resulted in a desired change in the user's behaviour. This paper describes this framework, how it has been tested using simulation and a short description of a test deployment.

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“…The proposed algorithm selects different features for different users to satisfy their usage behavior. Although there have been many research works solving the prediction problem in different domains, such as music items or playlist prediction [10], dynamic preference prediction [11], [12], location prediction [13], [14], [15], social links prediction [16], [17], and so on, the prediction methods are only based on analysing the usage history. In [18], the author selected features from multiple data streams, but the goal is to solve the communication problem in a distributed system.…”

Section: Related Workmentioning

confidence: 99%

On the Feature Discovery for App Usage Prediction in Smartphones

Liao

Peng

et al. 2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.

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Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction

Cited by 3 publications

References 12 publications

Exploiting Usage to Predict Instantaneous App Popularity

Exploiting Usage to Predict Instantaneous App Popularity

NudgeAlong: A Case Based Approach to Changing User Behaviour

On the Feature Discovery for App Usage Prediction in Smartphones

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