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.     

数据驱动的移动应用用户接受度建模与预测

应用市场（app market）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（software analytics）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、概率模型等方法验证用户接受度的可预测性,并讨论了预测结果与特征在移动应用开发过程中可能提供的指导.     

User-centred app design for speech sound disorders interventions with tablet computers

Traditional speech and language pathology practice (SLPP) faces challenges delivering effective and timely therapy due to long waiting lists, the need for regular practice outside the clinic and a lack of children’s motivation to engage in persistent practice. Technology has untapped potential to address these issues and improve SLPP. This paper describes the design of a tablet app for delivering technology-enhanced therapy for children with speech sound disorders and investigates the impact of the use of apps on SLPP. The initial design was informed by a nation-wide survey of speech-language pathologists (SLPs). The quantitative analysis disclosed that even though SLPs positively perceive mobile technology, they do not currently fully exploit it in their practice due to a lack of apps in their native language and the limited usefulness of apps in foreign languages. Using a user-centred design process, a multidisciplinary team created three prototypes and a final version of an app that has been tested in real therapeutic sessions during everyday practice and informed by feedback from SLPs and children. The observation analysis is presented based on an adaptation of Koole’s FRAME model. The qualitative findings indicate that SLPs identify mobile apps as enabling greater mobility, allowing new therapeutic approaches, creating possibilities for practice outside the therapeutic setting and increasing children’s motivation, supporting greater persistence to practise in the context of the therapy.     

Recreational athletes’ running motivations as predictors of their use of online fitness community features

Fitness wearables and apps provide users with quantified information about their exercise behaviour. Users often access this information on online fitness communities (OFCs) such as RunKeeper or Strava. These OFCs do not only provide feedback on the user’s performance but also offer social features. To date, little is known about the extent to which the different features in OFCs answer to users’ motivations to exercise. This study addresses this question, by examining (1) whether there are differences in motivations for running between OFC users and non-users and (2) whether the use of particular features is driven by particular running motivations. A survey study was conducted among 717 runners, of which 57% used an OFC to support running activities. Results demonstrate that OFC users are more achievement-oriented than non-OFC users, especially regarding the attainment of personal goals. OFC users with physical motivations (e.g. weight loss) use self-regulatory features more frequently, while runners with social motivations more often use features that afford them to share activities on social media. Achievement-oriented runners appreciate features that allow them to track their progress and interact with other OFC users. No relation was found between the use of OFC features and psychological motivations for running.     

An in-depth study of the promises and perils of mining GitHub

With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub’s event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub’s main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub’s registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.     

软件工程数据挖掘研究进展

随着计算机软件的规模不断扩大,手工获取、开发和维护软件所需的信息越来越困难。数据挖掘技术可从软件工程数据中自动发现所需信息,加快软件开发进程。对软件工程数据挖掘的研究进展进行了综述。概述了软件工程数据挖掘的基本概念与技术挑战;详细评述了在软件工程各个阶段,数据挖掘技术所能发现的信息/知识,以及获取这些信息/知识的意义、难点、步骤和方法,重点介绍了数据预处理和数据表示方法;对软件工程数据挖掘研究的发展趋势进行了展望。     

A multi-attribute probabilistic matrix factorization model for personalized recommendation

Recommendation systems can interpret personal preferences and recommend the most relevant choices to the benefit of countless users. Attempts to improve the performance of recommendation systems have hence been the focus of much research in an era of information explosion. As users would like to ask about shopping information with their friend in real life and plentiful information concerning items can help to improve the recommendation accuracy, traditional work on recommending based on users’ social relationships or the content of item tagged by users fails as recommending process relies on mining a user’s historical information as much as possible. This paper proposes a new recommending model incorporating the social relationship and content information of items (SC) based on probabilistic matrix factorization named SC-PMF (Probabilistic Matrix Factorization with Social relationship and Content of items). Meanwhile, we take full advantage of the scalability of probabilistic matrix factorization, which helps to overcome the often encountered problem of data sparsity. Experiments demonstrate that SC-PMF is scalable and outperforms several baselines (PMF, LDA, CTR, SocialMF) for recommending.     

时空聚类分析的普适性方法

时空聚类分析是时空数据挖掘领域近年来研究的热点问题,对于揭示时空要素的发展变化趋势、规律以及本质特征具有重要意义.目前,时空聚类分析的研究仍还初步,缺乏具有普适性的时空聚类分析方法.为此,本文首先建立了一套时空聚类分析的普适性理论方法框架.进而,借助时空统计学、智能计算等工具,提出了一种时空一体化的时空聚类方法.该方法很好地顾及了时空数据的时空耦合、时空相关与时空异质特征,避免了过多人为主观因素的干扰,时空聚类结果具有较好的可靠性.通过采用中国陆地区域42年(1951～1992)年平均气温时空数据进行分析,验证了本文提出的理论与方法的可行性与有效性.     

A hermeneutic interpretation of a controlled laboratory experiment: a case study of decision‐making with a group support system

We conduct a case study of a laboratory experiment involving a group support system and explain how it went awry. We take the perspectives of the experiment's human subjects and the researchers themselves as the basis on which to interpret what happened in the experiment. We interpret the researchers as imputing, to the human subjects, the ‘conduit model’ of communication and the ‘calculator model’ of human information processing, which together constitute an instance of Ricoeur's hermeneutic ‘world behind the text’. We interpret the human subjects as importing, into the laboratory, their socially constructed world of personal friends, their histories and even their popular culture – a world that is an instance of Ricoeur's hermeneutic ‘world in front of the text’. We explain the experiment's going awry as following from the researchers' not accounting for, much less being aware of, the disparity between the two worlds. In taking the human subjects and the researchers seriously as human beings, we make recommendations about how such experiments might be better conducted, particularly in information systems research.     

Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

Hardware-assisted credential management scheme for preventing private data analysis from cloning attacks

The majority of mobile apps use credentials to provide an automatic login function. Credentials are security tokens based on a user’s ID and password information. They are created for initial authentication, and this credential authentication then replaces user verification. However, because the credential management of most Android apps is currently very insecure, the duplication and use of another user’s credentials would allow an attacker to view personal information stored on the server. Therefore, in this paper, we analyze the vulnerability of some major mobile SNS apps to credential duplication that would enable access to personal information. To address the identified weaknesses, we propose a secure credential management scheme. The proposed scheme first differentiates the credential from the smart device using an external device. Using a security mechanism, the credential is then linked with the smart device. This ensures that the credential will be verified by the special smart device. Furthermore, based on experimental results using a prototype security mechanism, the proposed scheme is shown to be a very useful solution because of its minimal additional overhead.     

信息增值机制下在线医疗隐私保护策略

近年来我国经济水平和人民生活水平飞速发展,医疗水平和医疗技术相继取得了突破。随着“互联网+”对各大领域商业模式创新的不断推动和深化,“互联网+”医疗发展得到了快速推动。机器学习、数据挖掘等数据处理技术不断发展,在线医疗过程中用户个人医疗隐私数据泄露风险引起了广大研究者的关注。考虑信息的可推断性,采用贴现机制以描述博弈不同阶段间用户隐私信息价值的变化;结合在线医疗隐私保护动机领域研究现状,通过博弈分析以从隐私保护动机层面探究如何调动博弈双方主体的积极性。针对用户有强意愿继续使用在线医疗平台、间断性提供隐私的博弈特征,采用重复博弈方法以更好地刻画用户与在线医疗平台之间的博弈过程。得出博弈双方主体的倾向变化规律,分析不同模型参数条件下博弈模型的混合策略纳什均衡及随着博弈阶段的进行双方博弈策略的变化趋势,给出当参数满足 2(c_p-c_n)≥l_p(p_n-p_p)时,用户开始由选择“同意共享隐私数据”转为选择“拒绝共享隐私数据”的重复博弈阶段,并通过仿真实验对上述结论进行了验证。基于以上结论,分别从在线医疗平台视角和用户视角,针对在线医疗过程中如何从博弈双方隐私保护动机层面实现隐私保护给出了可行的政策性建议。     

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.     

SelfTrust: leveraging self-assessment for trust inference in Internetware

Internetware is envisioned as a new software paradigm for software development in platforms such as the Internet.The reliability of the developed software becomes a key challenge due to the open,dynamic and uncertain nature of such environment.To make the development more reliable,it is necessary to evaluate the trustworthiness of the resource providers or potential working partners.To this end,we propose a novel trust inference approach to evaluating the trustworthiness of potential partners to guide the software development in Internetware.The main insight of our approach is to employ the self-assessment information in order to improve the trust inference accuracy.Especially,we frst extend the balance theory and the status theory from social science to incorporate self-assessment,and then propose a machine learning framework to extract several features from the extended theories and infer trustworthiness scores based on these features.Experimental results on a real software developer network show that the self-assessment information truly helps to improve the accuracy of trust inference,and the proposed SelfTrust model is more accurate than other state-of-the-art methods.     

Hidden in plain sight: low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today’s society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX^© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users’ motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies’ specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training.     

无线体征监测系统的低功耗调度方法

基于超宽带(UWB)体域网和异构的生理传感器,设计了可对多种生理信息进行实时采集与处理的无线体征监测系统,并针对此类系统提出了基于体征状态机的低功耗调度方法,以便长期监测人体的健康与安全。在该调度方法中,协调器根据系统的体征状态机自适应地确定下一监测周期的传感器集合,并控制相应的生理传感器进行协同工作。仿真结果表明在同样的监测情况下,建议的调度方法有效减少了多生理传感器的不必要运行和无线传输的数据量,从而延长了整个监测系统的工作寿命。     

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.

     

基于本体的数据挖掘技术应用于商务智能中的实际应用

首先阐述了挖掘技术与商务智能的含义,指出了数据挖掘技术应用在商务智能中的意义,结合新时期我国各大企业的发展实际,对基于本体的数据挖掘技术应用在商务智能中的实际情况进行了分析,旨在利用数据挖掘技术,发挥出企业商务智能系统的优势,提高企业核心竞争力,促进企业长远发展。     

CSLabel: An Approach for Labelling Mobile App Reviews

Mobile apps (applications) have become a popular form of software, and the app reviews by users have become an important feedback resource. Users may raise some issues in their reviews when they use apps, such as a functional bug, a network lag, or a request for a feature. Understanding these issues can help developers to focus on users’ concerns, and help users to evaluate similar apps for download or purchase. However, we do not know which types of issues are raised in a review. Moreover, the amount of user reviews is huge and the nature of the reviews’ text is unstructured and informal. In this paper, we analyze 3 902 user reviews from 11 mobile apps in a Chinese app store — 360 Mobile Assistant, and uncover 17 issue types. Then, we propose an approach CSLabel that can label user reviews based on the raised issue types. CSLabel uses a cost-sensitive learning method to mitigate the effects of the imbalanced data, and optimizes the setting of the support vector machine (SVM) classifier’s kernel function. Results show that CSLabel can correctly label reviews with the precision of 66.5%, the recall of 69.8%, and the F₁ measure of 69.8%. In comparison with the state-of-the-art approach, CSLabel improves the precision by 14%, the recall by 30%, the F₁ measure by 22%. Finally, we apply our approach to two real scenarios: 1) we provide an overview of 1 076 786 user reviews from 1 100 apps in the 360 Mobile Assistant and 2) we find that some issue types have a negative correlation with users’ evaluation of apps.     

Rebooting democracy? Political data mining and biometric voter registration in Africa

The introduction of biometric voter registration and biometric voter identification on election day is a new trend in most African countries. This development in turn has necessitated massive political data mining. Yet, the nexus between elections and technology poses challenges on protection of personal information. This article offers a critical discussion of legal and regulatory frameworks that govern protection of personal information in an election context. Using the international standards for personal data protection and lessons from Kenya and Ghana, it notes that Tanzania does not have a systematic regime for personal data protection. This leaves voters’ personal data without adequate protection. Accordingly, the adoption of the biometric technology in the process of registration of voters creates greater potentials for violations of personal data than it was the case with the optical mark recognition technology.