Insight-centric Visualization Recommendation

Harris, Camille; Rossi, Ryan A.; Malik, Sana; Hoffswell, Jane; Du, Fan; Lee, Tak Yeon; Koh, Eunyee; Zhao, Handong

doi:10.48550/arxiv.2103.11297

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…However, the advantages of having social motivation and behavioral tailoring should not be ignored. The messages provided by informational systems are often generated by a discovery component [46] that analyzes data for patterns that are further filtered by a recommender component [46,47]. In the domain of physical health intervention, messages focus on different health measurements of users.…”

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Susaiyah,

Härmä,

Reiter

et al. 2024

Preprint

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Purpose: This study aims to investigate the impact of personalized health insights generated from wearable device data on users' health behaviors. The primary objective is to assess whether user feedback-driven algorithms enhance the relevance and effectiveness of health insights, ultimately influencing positive changes in users' daily activities. Methods: A two-month field study was conducted with 25 healthy volunteers using Mi Band 6 wearable devices. Participants were divided into test and control groups, and the test group received personalized insights recommended by a neural network-based algorithm fine-tuned by user feedback. The data collected included various health parameters such as calories burned, step count, heart rate, heart minutes, active minutes, sleep duration, sleep time and sleep segments. Insights were provided through a Telegram chatbot and user feedback was collected through a rating system. Results: The study revealed that the test group, which considered user feedback for insight recommendations, showed a significant improvement in daily activity compared to the control group. The relevance of the insights over time, as evidenced by feedback regression trends, showed a notable increase in the test group. Additional analyzes explored the relationship between insight delivery timing, user feedback, and delays, providing insights into user engagement patterns. Conclusion: This research highlights the effectiveness of personalized health insights generated from wearable data in positively influencing user health behaviors. Incorporating feedback from users into recommendation algorithms greatly enhances the relevance and effectiveness of insights, encouraging behavioral improvements. The results emphasize the significance of timing when providing insights and propose potential areas for future investigation, such as utilizing Graph Neural Networks to improve recommendation systems. In general, personalized insights from wearables have the potential to empower individuals to manage their health and well-being effectively.

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Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Susaiyah,

Härmä,

Reiter

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Visualization choices depend on the characteristics of the input dataset. To extract quantified characteristics (features) of datasets, we first surveyed prior studies of visualization recommendation [10], [32], [33] and visualization insight discovery [48], [49]. Based on the survey results, we categorized the dataset features into two types: single-column features and cross-column features, as shown in Figure 4 A and B .…”

Section: Feature Extractionmentioning

confidence: 99%

Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation

Zhang

Zhu

Wang

2023

Lecture Notes in Computer Science

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Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis.

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Insight-centric Visualization Recommendation

Cited by 2 publications

References 38 publications

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation

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