System U

Badenes, Hernan; Bengualid, Mateo; Chen, Jilin; Gou, Liang; Haber, Eben M.; Mahmud, Jalal; Nichols, Jeffrey; Pal, Aditya; Schoudt, Jerald; Smith, Barton A.; Yang, Xuan; Yang, Huahai; Zhou, Michelle X.

doi:10.1145/2645710.2645719

Cited by 22 publications

(4 citation statements)

References 5 publications

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“…Several recommendation tools have represented and exposed the user model behind the recommendation mechanism [21,[29][30][31]. However, scrutability is lacking in these tools.…”

Section: Explainable Recommender Systemsmentioning

confidence: 99%

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

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This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.

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“…Several recommendation tools have represented and exposed the user model behind the recommendation mechanism [21,[29][30][31]. However, scrutability is lacking in these tools.…”

Section: Explainable Recommender Systemsmentioning

confidence: 99%

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

View full text Add to dashboard Cite

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“…personality modelling. We have used pre-trained behavioural and emotional personality models 24,62 to determine the well-studied characteristics of influential users. The open vocabulary-based approach, tokenize the recent user response (here, maximum 3,000) to form n-dimensional vector representation of tokens 63 .…”

Section: Identification Of Influential Users the Influential Nodes Omentioning

confidence: 99%

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Agarwal

Toshniwal

2020

Sci Rep

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With the availability of smart devices and affordable data plans, social media platforms have become the primary source of information dissemination across geographically dispersed users/locations. it has shown great potential across different application domains including event detection, opinion analysis, recommendation, and prediction. However, the process of extracting useful information from the collected voluminous social media data during natural hazards is a standing problem that needs significant attention from the research community. The fine-grained knowledge detailing users' participation in information spreading could be advantageous in developing a reliable social network for the adverse events (Natural Hazards, Man-made attacks etc.). However, there has been no such findings related to identification of leader and their leadership characteristics associated with natural hazards in previous studies. We have collected 20.6 million tweets which were posted by 5.3 million users, during distinct devastating hazards namely-Floods, Hurricane, Earthquake and Typhoons. To achieve the goal, we divided our work in to three parts. Firstly, classify the collected crises data into four domains i.e resource, causality, news, and sympathy by employing deeper recurrent neural network model. Secondly, we used statistical physics of complex network to recognize local as well as global prominent leaders. At last, we curate leadership characteristics in terms of their big five personality traits and emotional traits. Our experimental, results find evidence that local leadership behaviour characteristics are significantly different from global potentials. Where as we also finds that some behaviour traits were certain to classified domains (resource, causality, news, and sympathy) and some were certain to hazard divisions, though emotional characteristics remained consistent. Later, we conclude that local potentials leaders have comparatively higher emotional strength. Furthermore, when the complete local network structure is unavailable, we find that the dynamic rank is reliable indexing proxy for local potentials. The current study, provide useful insight to understand how leadership characteristics are influenced to hazards, domains and centrality of users. Nowadays, billions of active users over worldwide use social network platforms (SNPs) to interact with each other and shares their day to day activity over platforms like Twitter, Facebook, Tumbler and many more. SNPs have an ample amount of real-time information sources pertinent to real-world events, predominately during any natural hazards or mass crises. These platforms play a vital role to disseminate any information among users through online social interaction 1-3. The previous study on information dissemination reveals that few influential knowledge spreaders (IKs) have the characteristics that can shape millions of users viewpoint 4. Therefore identification of IKs holds immense practical importance and also in recent years it attracted the considerable at...

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“…Apart from this demographic profiles based on age, gender etc. too has been used, but so far no work has been done in empirically demonstrating the benefits of usage of Human values to address the data sparsity problem in RS (Badenes et al, 2014). Srivastava,A.,Bala,P.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

Srivastava

Bala

Kumar

2017

JISTEM

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With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of sufficient feedback data these systems suffer from sparsity problem which leads to decline in their prediction efficiency. In this work, we have proposed and empirically demonstrated how the Transfer Learning approach using five dimensions of basic human values can be successfully used to alleviate the sparsity problem and increase the efficiency of recommender system algorithms.

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System U

Cited by 22 publications

References 5 publications

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

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