Understanding the evaluation of mHealth app features based on a cross-country Kano analysis

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

     

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.     

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.     

Recommendation algorithm of the app store by using semantic relations between apps

In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.     

A heterogeneous hidden Markov model for mobile app recommendation

Recent proliferation of ubiquitous smart phones has led to the emergence of a wide variety of apps. Selecting apps through keyword search or recommendations from friends or social networks (e.g., Facebook) may not match the real preferences of users, especially when the need is just-in-time and context specific. Although there are many collaborative filtering approaches that are capable of generating time-aware recommendations, most of them work on modeling of the time stamps (the time that events happen) rather than modeling of the sequential patterns (in cases that time stamps are not available) as well as investigating the factors behind those patterns. In this paper, we propose a mechanism for modeling three important factors governing the app installation of smart phone users: (1) short-term context, (2) co-installation pattern, and (3) random choice. Specifically, we use a hidden Markov model equipped with heterogeneous emission distributions to incorporate these factors. Apps being installed are probabilistically categorized into one of these factors, and app recommendations for users are carried out accordingly. This coherent model can be inferred effectively by using Gibbs sampling. The formulation has a significant advantage that the performance is less sensitive to data sparsity and incompleteness. Empirical results show that it has higher performance in recommending mobile apps to smart phone users, measured in terms of precision and area under the ROC curve (AUC). Besides, the proposed model allows the nature of the apps, with respect to the three factors, to be revealed as well as the extent to which each user is affected by the three factors to be inferred, providing additional insights on the users’ behavior.     

Order matters: Breaking cognitive lock-in through occasional preferential use of a rival app

Although growing attention has been paid to the use of competing technologies, little is known about how occasional preferential use of the rival system (OPUR) affects individuals’ adoption of the dominant system. In this study, we investigate how OPUR breaks the cognitive lock-in of the dominant system in the app context. The results demonstrate that supplementing OPUR reduces (increases) user access to the dominant (rival) app. In addition, the OPUR effect is more salient for high-demand and female users. Our results demonstrate that OPUR provides a great opportunity for catching-up apps to offset the first-mover advantage of dominant apps.     

An intelligent recommendation system using gaze and emotion detection

Recently, recommendation system has become popular in many e-commerce websites. It helps users by suggesting products which they could buy. Existing work till now uses past feedback of user, similarity of other users’ buying pattern, or a hybrid approach in which both type of information is used. But the pitfall of these approaches is that there is a need to collect and process huge amount of data for good recommendation. This paper is aimed at developing an efficient recommendation system by incorporating user’s emotion and interest to provide good recommendations. The proposed system does not require any of aforementioned data and works without the continuous and interminable attention of the user. In this framework, we capture user’s eye-gaze and facial expression while exploring websites through inexpensive, visible light “webcam”. The eye-gaze detection method uses pupil-center extraction of both eyes and calculates the reference point through a joint probability. The facial expression uses landmark points of face and analyzes the emotion of the user. Both methods work in approximate real time and the proposed framework thus provides intelligent recommendations on-the-fly without requirement of feedback and buying patterns of users.

     

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.     

Evaluation of a mobile mindfulness app distributed through on-line stores: A 4-week study

Recently, interactive approaches aimed at helping people practice mindfulness have appeared in the literature. However, the few available user studies for such approaches focus only on short-term effects and are carried out in a lab or in a similar artificial setting. In this study, we aim instead at assessing the effectiveness of a mobile mindfulness app when used by people in their everyday contexts and over a prolonged period of time. People could participate in the study by downloading the app from Apple’s App Store as well as Google Play and by answering a mindfulness questionnaire at three pre-set times over a 4-week period. Moreover, the app automatically collected usage data each time it was used and qualitative feedback at the end of the study. Results reveal that users with no or minimal experience with meditation significantly increased their level of mindfulness over the 4-week study period. Moreover, the qualitative feedback provided by participants indicates that the app was positively perceived as beautiful and its usage elicited positive feelings in most of them. We discuss possible factors that could have contributed to the obtained results. Finally, we analyze how many users abandoned the study and at what times, comparing such data with other studies based on app stores distribution, and giving possible reasons.     

Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.     

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.     

Evaluating mobile app usage by service sector micro and small enterprises in Nigeria: an abductive approach

ABSTRACT

This research investigated how mobile apps influence the dynamic capabilities of service sector micro and small enterprises (MSEs) in Lagos, Nigeria. Using an abductive method, data from 388 service sector MSEs was examined through exploratory factor analysis. The resultant model suggests that mobile app usage barely increases the absorptive capability (integrating new learning into the organization) of MSEs; rather, it strongly influences the ability to seize opportunities. The result implies that mobile app usage by service sector MSEs in Lagos deviates from the conventional views on the micro foundations of the dynamic capability framework, which argues that sensed opportunities are first analysed (shaped) before resources are deployed towards their maximization. These findings suggest that the service sector MSEs in Lagos seldom scrutinize opportunities before deploying resources to seize them. This study extends IS literature on how mobile apps help MSEs to exploit business opportunities in resource-constrained contexts.     

融合时间加权信任与用户偏好的协同过滤算法

针对现有的协同过滤推荐算法中存在评分数据稀疏和用户兴趣动态变化的问题,提出了融合时间加权信任与用户偏好的协同过滤算法.考虑到用户评分时间的不均匀,对时间权重进行改进,并将其融入到直接信任计算中,缓解用户兴趣动态变化的问题.通过信任传递得到的间接信任以及建立用户对项目标签的偏好矩阵得到用户之间的偏好相似度来缓解数据的稀疏...     

A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN

Point of interest (POI) recommendation problem in location based social network (LBSN) is of great importance and the challenge lies in the data sparsity, implicit user feedback and personalized preference. To improve the precision of recommendation, a tensor decomposition based collaborative filtering (TDCF) algorithm is proposed for POI recommendation. Tensor decomposition algorithm is utilized to fill the missing values in tensor (user-category-time). Specifically, locations are replaced by location categories to reduce dimension in the first phase, which effectively solves the problem of data sparsity. In the second phase, we get the preference rating of users to POIs based on time and user similarity computation and hypertext induced topic search (HITS) algorithm with spatial constraints, respectively. Finally the user’s preference score of locations are determined by two items with different weights, and the Top-N locations are the recommendation results for a user to visit at a given time. Experimental results on two LBSN datasets demonstrate that the proposed model gets much higher precision and recall value than the other three recommendation methods.

     

A novel application recommendation method combining social relationship and trust relationship for future internet of things

Traditional collaborative filtering methods always utilize Cosine and Pearson methods to calculate the similarity of users. When the nearest neighbor doesn’t comment the predicted item, then the nearest neighbor has no influence on results, thus affecting the accuracy of collaborative filtering recommendation. And the traditional recommendation systems always have the problems of data sparsity, cold start and so on. In this paper, we consider social relationship and trust relationship, and put forward a novel application recommendation method that combines users’ social relationship and trust relationship. Specifically, we combine social relationship and user preference towards applications to calculate similarity score, we fuse the trust relationship based on familiarity and user reputation to calculate trust score. The final prediction score is calculated by fusing similar relationship and trust relationship properly. And the proposed method can effectively improve accuracy of recommendations.

     

一种融合多维信息的移动社区发现方法

为解决传统社区发现算法难适用于大型复杂异质的移动网络的问题,利用移动网络使用详单数据（Usage Detail Record, UDR）和移动用户社交数据构建网络模型,提出一种融合多维信息的移动社区发现方法BNMF-NF。该方法综合考虑用户社交关系和时空行为,给出用户社交相似度、位置分布相似度和主题偏好相似度,利用加权网络融合方法融合多维相似关系构建用户相似网络,并运用有界非负矩阵分解技术实现社区结构的检测。在Foursquare和电信数据集上的实验结果表明,BNMF-NF方法能够有效发现移动网络中用户社区结构。     

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.     

Examining the impacts of fitness app features on user well-being

Drawing on the self-regulation theory, the current paper explores the impacts of two types of fitness app feature sets (i.e., personal-oriented and social-oriented features) on users’ health behavior and well-being. The results from fitness app users show that both personal-oriented features and social-oriented features of fitness apps can significantly improve exercise adherence and social engagement of users. Users’ exercise proficiency level negatively moderates the relationship between social-oriented features and (a) exercise adherence and (b) social engagement. High levels of social engagement promote users’ physical adherence to exercises. Exercise adherence and social engagement both enhance users’ subjective well-being, but their impacts on different dimensions of well-being vary. Furthermore, regardless of specific features, sufficient use of fitness apps, in general, can significantly help users lead more positive and healthier lives by maintaining exercise adherence, reducing emotional exhaustion, and improving their satisfaction with the overall quality of life. Our findings offer important insights into the underlying mechanisms that help explain fitness app features on users’ well-being, and on a practical level, provide suggestions for mobile app developers in designing better fitness app products and for exercisers in optimizing the benefits of fitness technology adoption.     

Exploring privacy paradox in information-sensitive mobile app adoption: A cross-cultural comparison

This paper proposes and tests a conceptual model of private-information sensitive mobile app adoption utilizing privacy calculus approach. It also explores the role of personality in affecting perceived benefits of using mobile apps and compares the findings across two countries: the US and China. Irrespective of the cultural environment, millennial mobile app users download apps that require access to sensitive personal information in order to satisfy their informational and social (but not entertainment) needs. Perceived privacy concern does not influence adoption or future use of private-information sensitive apps. Extraversion and agreeableness are positively related to user perceptions of benefits obtained from using apps.     

HOPE: a hybrid deep neural model for out-of-town next POI recommendation

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.