User Interaction Modeling and Profile Extraction in Interactive Systems: A Groupware Application Case Study

Tîrnăucă, Cristina; Duque, Rafael; Montaña, José Luis

doi:10.3390/s17071669

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Compared to other analytical themes, these 3 design features focused on the interactivity of mHealth interventions, including user-to-technology interactions and user-to-user interactions. User-to-technology communication refers to having the user input information about themselves to which the tool provides a tailored response [ 75 ]. Two analytical themes, personalization and reinforcement, pertain to the interaction between users and technology.…”

Section: Discussionmentioning

confidence: 99%

Design Features for Improving Mobile Health Intervention User Engagement: Systematic Review and Thematic Analysis

et al. 2020

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Background Well-designed mobile health (mHealth) interventions support a positive user experience; however, a high rate of disengagement has been reported as a common concern regarding mHealth interventions. To address this issue, it is necessary to summarize the design features that improve user engagement based on research over the past 10 years, during which time the popularity of mHealth interventions has rapidly increased due to the use of smartphones. Objective The aim of this review was to answer the question “Which design features improve user engagement with mHealth interventions?” by summarizing published literature with the purpose of guiding the design of future mHealth interventions. Methods This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Databases, namely, PubMed, Web of Science, Cochrane Library, Ovid EMBASE, and Ovid PsycINFO, were searched for English and Chinese language papers published from January 2009 to June 2019. Thematic analysis was undertaken to assess the design features in eligible studies. The Mixed Methods Appraisal Tool was used to assess study quality. Results A total of 35 articles were included. The investigated mHealth interventions were mainly used in unhealthy lifestyle (n=17) and chronic disease (n=10) prevention programs. Mobile phone apps (n=24) were the most common delivery method. Qualitative (n=22) and mixed methods (n=9) designs were widely represented. We identified the following 7 themes that influenced user engagement: personalization (n=29), reinforcement (n=23), communication (n=20), navigation (n=17), credibility (n=16), message presentation (n=16), and interface aesthetics (n=7). A checklist was developed that contained these 7 design features and 29 corresponding specific implementations derived from the studies. Conclusions This systematic review and thematic synthesis identified useful design features that make an mHealth intervention more user friendly. We generated a checklist with evidence-based items to enable developers to use our findings easily. Future evaluations should use more robust quantitative approaches to elucidate the relationships between design features and user engagement.

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Section: Discussionmentioning

confidence: 99%

Design Features for Improving Mobile Health Intervention User Engagement: Systematic Review and Thematic Analysis

et al. 2020

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“…By increasing the values of r with a step 0.2, we observe the optimal group radius for datasets. Base on diverse group, we model group user profile vector according to Equation (10), and compute the corresponding interest score of a new subject for a user. We can choose top k subjects according to the interest score of GUP and make micro-blog recommendations involved in these subjects for target users.…”

Section: Recommendation Strategiesmentioning

confidence: 99%

“…), Benouaret et al [9] designed a hybrid recommender system for mobile devices to improve the visitors' museum experience. With a groupware application (synchronous or asynchronous), Duque et al [10] clustered similar users' model to obtain groups of users for the automatic generation of user interaction models.…”

Section: Introductionmentioning

confidence: 99%

“…Diverse interest mining from representative users can be used to decision analysis, vote, e-commerce, web search engines, socialized recommender systems and museum visitors' quality of experience and other fields [4,16,[18][19][20][21]. Thus, it is reasonable that the followees could be replaced by a dissimilar and representative group to GUP for recommendation [7,10,19]. The GUP can efficiently provide diverse interests for target users than conventional UPs.…”

Section: Introductionmentioning

confidence: 99%

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Group User Profile Modeling Based on Neural Word Embeddings in Social Networks

Zheng

Sangaiah

2018

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How to find a user’s interest from similar users a fundamental research problems in socialized recommender systems. Despite significant advances, there exists diversity loss for the majority of recommender systems. With this paper, for expanding the user’s interest, we overcome this challenge by using representative and diverse similar users from followees. First, we model a personal user profile vector via word2vec and term frequency mechanisms. According to user profiles and their follow relationships, we compute content interaction similarity and follow interaction similarity. Second, by combining two kinds of interaction similarity, we calculate the social similarity and discover a diverse group with coverage and dissimilarity. The users in a diverse group can distinguish each other and cover the whole followees, which can model a group user profile (GUP). Then, by tracking the changes of followee set, we heuristically adjust the number of diverse group users and construct an adaptive GUP. Finally, we conduct experiments on Sina Weibo datasets for recommendation, and the experimental results demonstrate that the proposed GUP outperforms conventional approaches for diverse recommendation.

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“…The framework incorporates the tool that was developed by Tîrnȃucȃ et al [40] to visualize through graphs those sequences of interactions performed by users that would be associated with poor usability levels according to the prediction of the neural network. In this way, the user of the framework can observe the sequences of interactions that are related to these low levels of usability.…”

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confidence: 99%

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

et al. 2021

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Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions’ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human–machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system’s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.

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User Interaction Modeling and Profile Extraction in Interactive Systems: A Groupware Application Case Study

Cited by 5 publications

References 23 publications

Design Features for Improving Mobile Health Intervention User Engagement: Systematic Review and Thematic Analysis

Design Features for Improving Mobile Health Intervention User Engagement: Systematic Review and Thematic Analysis

Group User Profile Modeling Based on Neural Word Embeddings in Social Networks

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

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