Personalized app recommendation based on app permissions

With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. How to help users to select applications they prefer has become a hot topic in recommendation algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay more attention to permissions of those applications, because of some privacy and security reasons. There are few recommendation algorithms which take account of apps’ permissions, functionalities and users’ interests altogether. Some of them only consider permissions while neglecting the users’ interests, others just perform linear combination of apps’ permissions, functionalities and users’ interests to implement top-N recommendation. In this paper, we devise a recommendation method based on both permissions and functionalities. After demonstrating the correlation of apps’ permissions and users’ interests, we design an app risk score calculating method ARSM based on app-permission bipartite graph model. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably.     

Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

     

微信小程序用户授权登录机制研究和实现

微信小程序的出现,一方面缓解了用户手机安装大量APP浪费手机存储资源并导致手机速度变慢的问题,另一方面,也减轻了开发者为不同手机操作系统(Android,iOS)分别开发程序的工作负担。微信小程序应用开发是以MVC模式的JSON作为数据交换格式的以WEB开发为基础的开发技术,但是也有很多不同于以往WEB开发的地方,尤其是用户授权登录方面,用户认证信息需要在微信小程序、开发者服务器和微信接口服务器之间传递,这个过程中要考虑用户认证信息传递的流程和数据安全问题。文章研究了这两个问题并在一个应用中做了具体实现。     

数据驱动的移动应用用户接受度建模与预测

应用市场(appmarket)已经成为互联网环境下软件应用开发和交付的一种主流模式.相对于传统模式,应用市场模式下,软件的交付周期更短,用户的反馈更快,最终用户和开发者之间的联系更加紧密和直接.为应对激烈的竞争和动态演变的用户需求,移动应用开发者必须以快速迭代的方式不断更新应用,修复错误缺陷,完善应用质量,提升用户体验.因此,如何正确和综合理解用户对软件的接受程度(简称用户接受度),是应用市场模式下软件开发需考量的重要因素.近年来兴起的软件解析学(softwareanalytics)关注大数据分析技术在软件行业中的具体应用,对软件生命周期中大规模、多种类的相关数据进行挖掘和分析,被认为是帮助开发者提取有效信息、作出正确决策的有效途径.从软件解析学的角度,首先论证了为移动应用构建综合的用户接受度指标模型的必要性和可行性,并从用户评价数据、操作数据、交互行为数据这3个维度给出基本的用户接受度指标.在此基础上,使用大规模真实数据集,在目标用户群体预测、用户规模预测和更新效果预测等典型的用户接受度指标预测问题中,结合具体指标,提取移动应用生命周期不同阶段的重要特征,以协同过滤、回归融合、概率模...     

Model-driven development of mobile applications for Android and iOS supporting role-based app variability

Rapidly increasing numbers of applications and users make the development of mobile applications to one of the most promising fields in software engineering. Due to short time to market, differing platforms, and fast emerging technologies, mobile application development faces typical challenges where model-driven development (MDD) can help. We present a modeling language and an infrastructure for the MDD of native apps in Android and iOS. Our approach allows a flexible app development on different abstraction levels: compact modeling of standard app elements such as standard data management and increasingly detailed modeling of individual elements to cover, for example, specific behavior. Moreover, a kind of variability modeling is supported such that mobile apps with variants can be developed. We demonstrate our MDD approach with several apps including a conference app, a museum guide with augmented reality functionality, and a SmartPlug.     

Mining smartphone data for app usage prediction and recommendations: A survey

Smartphones nowadays have become indispensable personal gadgets to support our activities in almost every aspect of our lives. Thanks to the tremendous advancement of smartphone technologies, platforms, as well as the enthusiasm of individual developers, numerous mobile applications (apps) have been created to serve a wide range of usage purposes, making our daily life more convenient. While these apps are used, data logs are typically generated and ambience context is recorded forming a rich data source of the smartphone users’ behaviors. In this paper, we survey existing studies on mining smartphone data for uncovering app usage patterns leveraging such a data source. Our discussions of the studies are organized according to two main research streams, namely app usage prediction and app recommendations alongside a few other related studies. Finally, we also present several challenges and opportunities in the emerging area of mining smartphone usage patterns.     

Multi-objective mobile app recommendation: A system-level collaboration approach

The tremendous increase of mobile apps has given rise to the significant challenge of app discovery. To alleviate such a challenge, recommender systems are employed. However, the development of recommender systems for mobile apps is at a slow pace. One main reason is that a general framework for efficient development is still missing. Meanwhile, most existing systems mainly focus on single objective recommendations, which only reflect monotonous app needs of users. For such reasons, we initially present a general framework for developing mobile app recommender systems, which leverages the multi-objective approach and the system-level collaboration strategy. Our framework thus can satisfy ranges of app needs of users by integrating the strengths of various recommender systems. To implement the framework, we originally introduce the method of swarm intelligence to the recommendation of mobile apps. To be detailed, we firstly present a new set based optimization problem which is originated from the collaborative app recommendation. We then propose a novel set based Particle Swarm Optimization (PSO) algorithm, namely, the Cylinder Filling Set based PSO, to address such a problem. Furthermore, we implement the algorithm based on three popular mobile app recommender systems and conduct evaluations. Results verify that our framework and algorithm are with promising performance from both the effectiveness and efficiency.     

Network-based detection of Android malicious apps

Users leverage mobile devices for their daily Internet needs by running various mobile applications (apps) such as social networking, e-mailing, news-reading, and video/audio streaming. Mobile device have become major targets for malicious apps due to their heavy network activity and is a research challenge in the current era. The majority of the research reported in the literature is focused on host-based systems rather than the network-based; unable to detect malicious activities occurring on mobile device through the Internet. This paper presents a detection app model for classification of apps. We investigate the accuracy of various machine learning models, in the context of known and unknown apps, benign and normal apps, with or without encrypted message-based app, and operating system version independence of classification. The best resulted machine learning(ML)-based model is embedded into the detection app for efficient and effective detection. We collect a dataset of network activities of 18 different malware families-based apps and 14 genuine apps and use it to develop ML-based detectors. We show that, it is possible to detect malicious app using network traces with the traditional ML techniques, and results revealed the accuracy (95–99.9 %) in detection of apps in different scenarios. The model proposed is proved efficient and suitable for mobile devices. Due to the widespread penetration of Android OS into the market, it has become the main target for the attackers. Hence, the proposed system is deployed on Android environment.     

Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention

The explosive global adoption of mobile applications (i.e., apps) has been fraught with security and privacy issues. App users typically have a poor understanding of information security; worse, they routinely ignore security notifications designed to increase security on apps. By considering both mobile app interface usability and mobile security notification (MSN) design, we investigate how security perceptions of apps are formed and how these perceptions influence users’ intentions to continue using apps. Accordingly, we designed and conducted a set of controlled survey experiments with 317 participants in different MSN interface scenarios by manipulating the types of MSN interfaces (i.e., high vs. low disruption), the context (hedonic vs. utilitarian scenarios), and the degree of MSN intrusiveness (high vs. low intrusiveness). We found that both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app. In addition, we identified an important conundrum: disruptive MSNs—a common approach to delivering MSNs—irritate users and negatively influence their perceptions of app security. Thus, our results directly challenge current practice. If these results hold, current practice should shift away from MSNs that interrupt task performance.     

Why do young people use fitness apps? Cognitive characteristics and app quality

This study examined the effects of young adults’ social-cognitive characteristics and fitness apps’ quality-related characteristics on users’ intention to continue using apps. This study used a self-report questionnaire to collect data from 201 participants between November 29 and December 16, 2016. PASW/WIN 20.0 was used to perform Pearson’s correlation analysis, and hierarchical multiple regression. Results showed that users’ social-cognitive characteristics and the app’ quality-related characteristics accounted for 39.3 and 1.6% of users’ intention to continue using fitness apps, respectively. Social-cognitive characteristics included quality-related app characteristics, which explained 40.9% of users’ intention to continue using the apps. Self-efficacy, innovative propensity, outcome expectations, and engagement were key variables affecting the intention to continue using apps. Therefore, it is suggested that researchers or healthcare providers who want to utilize fitness apps for young adults could invest time and effort in the selection of existing high-quality apps and design intervention programs to stimulate users’ social-cognitive factors.     

Forensic taxonomy of android productivity apps

Android productivity apps have provided the facility of having a constantly accessible and productive workforce to the information and work capabilities needed by the users. With hundreds of productivity apps available in the Android app market, it is necessary to develop a taxonomy for the forensic investigators and the end users to allow them to know what personal data remnants are available from the productivity apps. In this paper, 30 popular Android productivity apps were examined. A logical extraction of the Android phone was collected by using a well-known mobile forensic tool- XRY to extract various information of forensic interest such as user email ID and list of tasks. Based on the findings, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is proposed with the app categories in one dimension and the classes of artefacts in the other dimension. The artefacts identified in the study of the apps are summarised using the taxonomy. In addition, a comparison with the existing forensic taxonomies of different categories of Android apps is provided to facilitate timely collection and analysis of evidentiary materials from mobile devices.     

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.     

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the overall impression of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 79% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.     

Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.     

融合主题模型和协同过滤的多样化移动应用推荐

随着移动应用的急速增长,手机助手等移动应用获取平台也面临着信息过载的问题.面对大量的移动应用,用户很难找到想到的或适合的应用,而另一方面长尾应用淹没在资源池中不被人所知.已有推荐方法多注重推荐准确率,忽视多样性,推荐结果中多是下载量高的应用,使得推荐系统的数据积累越来越偏向于热门应用,导致长期的推荐效果越来越差.针对此问题,本文首先改进了两个推荐方法,提出了将用户的主题模型和应用的主题模型与MF相结合的LDA_MF模型,以及将应用的标签信息和用户行为数据同时加以考虑的LDA_CF算法.为了结合不同算法的优点,在保证推荐准确率的条件下提升推荐结果的多样性,我们提出了融合LDA_MF、LDA_CF以及经典的基于物品的协同过滤模型的混合推荐算法.文章使用真实的大数据评测所提推荐算法,结果显示所提推荐方法能够得到推荐多样性更好且准确率高的结果.     

An analysis of the relationship between quality and user acceptance in smartphone apps

This study investigates consumer intentions within the smartphone app environment. More specifically, it studies the factors influencing the intention to use banking apps based on the smartphone by employing the information system success model and a revised technology acceptance model. The study examines how quality factors and attitudes toward mobile apps-based banking influence the intention to use banking apps, and whether trust influences the relationship between quality factors and intention to use. In it, we collect data from 520 users and estimate the structural model. The results indicate that attitudes to mobile apps-based banking, as well as information and service quality, affect consumers’ intention to use banking apps. We further confirm that three particular quality factors, moderated by trust, affect the intention to use these apps. This study helps to explain consumers’ mobile apps-based banking behaviours, by combining the information system success model with a technology acceptance model.     

Texting and walking: a controlled field study of crossing behaviours and inattentional blindness in Taiwan

The paper investigates the effects of phone use (talking, texting, and listening to music) on the street-crossing behaviours of pedestrians and their inattentional blindness in Taiwan. Recent handsets with touchscreens, as well as more advanced features including multimedia, and mobile applications (apps), exacerbate problems relating to cognitive distraction and reduced situation awareness. A controlled field study using video cameras was conducted for observing pedestrians’ crossing behaviours (e.g. crossing time, sudden stops, looking both ways before crossing, and disobeying traffic signals). Pedestrians were classified into two groups: experimental group (talking, texting, and listening to music) and control group (no phone use). Pedestrians’ inattentional blindness was examined by evaluating whether they saw and heard an unusual object (i.e. a clown) nearby. The results indicate that the proportions of unsafe crossing behaviours (e.g. sudden stops, disobeying traffic signals, and not looking both ways before crossing) were higher among distracted individuals and more pronounced among those using instant-messaging apps. These instant-message app users were the least likely to see the clown, and music listeners were the least likely to hear the horn that the clown was honking. Contributing factors to unsafe behaviours include being a student, having a phone screen of 5?inches or larger, and having unlimited 3G Internet access. Texting message via apps was the leading factor on unsafe crossing behaviours of pedestrians and their inattentional blindness.     

Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.     

“Masculine Guys Only”: The effects of femmephobic mobile dating application profiles on partner selection for men who have sex with men

Mobile dating applications (apps) have changed the way gay men find others in their geographic area for sexual activity and romantic relationships. Many of these apps are branded in relation to traditional masculinity and have become a breeding ground for femmephobic, or anti-effeminate, language. Past research has not examined the effects of femmephobic language in social networking apps designed for men who have sex with men (MSM) on app users' perceptions. This research employed an online experiment of 143 MSM app users to test how users respond to femmephobic and non-femmephobic language use in MSM dating profiles. Participants rated the profile users, as well as reported their desire to meet the user in an offline context. Results indicated that the use of femmephobic language in dating profiles affects a potential partner's perceived intelligence, sexual confidence, and dateability, as well as one's desire to meet potential partners offline for friendship or romantic purposes. Anti-effeminacy was an important moderator of the main effect.