A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques

Gençer, Merve; Bilgin, Gökhan; Zan, Özgür; Voyvodaoğlu, Tansel

doi:10.1007/978-3-642-39253-5_72

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…We agree on that; however, we also suggest developers pay attention to users' initial experiences with the product, particularly for mobile apps. Apps are suggested to have shorter life cycle; evidence shows that 25% of users will log into new apps only once, and 80% of first-time users will decide whether to continue using the application within three minutes of use [18]. Unfortunately, we have not seen any UX research that has emphasized the importance of first-time user experience, nor have we seen research that has studied both the first-time and the long-term experience.…”

Section: Discussionmentioning

confidence: 99%

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An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues

Liu

Wei

2019

Sustainability

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We introduce an approach that supports researchers and practitioners to determine the quality of first-time user experience (FTUX) and long-term user experience (LTUX), as well as to identify critical issues with these two types of UX. The product we chose to study is a mobile fitness application. Mobile apps tend to have a much shorter service life than most other products; thus, the developers/designers need to pay great attention to both first-time and long-term user experience. This study is based on a multi-method approach. We employed the AttrakDiff questionnaire to assess users’ first impressions of the app, and the UX Curve method to evaluate how users’ experience of the app has changed over time. Besides the quantitative data, which helped to determine the quality of user experience, we also collected qualitative data during two interviews with participants, and focused on the issues that predominantly deteriorated user experience. A four-coordinate plane tool was designed later in the data analysis process that combined the two kinds of user experience data at the same time, which led to a qualitative positioning of the user experience status of a certain product. The model was further successfully adopted in the identification of user experience issues of an online fitness application.

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Section: Discussionmentioning

confidence: 99%

“…This is especially the case for mobile apps. Research shows that 25% of new users will log in to new apps only once, and 80% of new users will decide whether to continue using the app within three minutes of use [18]. Similarly, Flurry, an iPhone app metrics company, found that a free iPhone application loses 95% of users after one month [18].…”

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confidence: 99%

An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues

Liu

Wei

2019

Sustainability

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“…However, the less intrinsically engaging a technology proves, the more likely proactive strategies may be required to enable a habit of use. These strategies can include mobile phone notifications, email reminders, scheduled incentives, and reward tiers (Gençer et al 2013).…”

Section: Promoting Repeated Usementioning

confidence: 99%

Engagement in HCI

Doherty

2018

ACM Comput. Surv.

168

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Engaging users is a priority for designers of products and services of every kind. The need to understand users' experiences has motivated a focus on user engagement across computer science. However, to date, there has been limited review of how Human-Computer Interaction and computer science research interprets and employs the concept. Questions persist concerning its conception, abstraction, and measurement. This article presents a systematic review of engagement spanning a corpus of 351 articles and 102 definitions. We map the current state of engagement research, including the diverse interpretation, theory, and measurement of the concept. We describe the ecology of engagement and strategies for the design of engaging experiences, discuss the value of the concept and its relationship to other terms, and present a set of guidelines and opportunities for future research.

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“…We can recommend nearby restaurants, shopping malls, and parks based on location, and the function is implemented in many map applications like Google Maps. For merchants, understanding user behavior is very helpful to proactively provide potential services and increase customer engagements [1,2]. To take advantage of the data analysis benefits, the first step is to figure out customers' consumption patterns.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

Zhang¹,

Xiao²,

Wan³

et al. 2017

Mobile Information Systems

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With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

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A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques

Cited by 6 publications

References 4 publications

An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues

An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues

Engagement in HCI

A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

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