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2018
Cecilia Di Sciascio; Peter Brusilovsky; Christoph Trattner; Eduardo Veas
The Roadmap to User-Controllable Social Exploratory Search Journal Article
In: ACM Transactions on Interactive Intelligent Systems, pp. 1-37, 2018, (Pre SFI).
Abstract | BibTeX | Tags: CCS Concepts, Computer systems organization, Embedded systems, Network reliability;, Networks, Redundancy, Robotics | Links:
@article{Sciascio2018,
title = {The Roadmap to User-Controllable Social Exploratory Search},
author = {Cecilia Di Sciascio and Peter Brusilovsky and Christoph Trattner and Eduardo Veas},
url = {https://www.christophtrattner.info/pubs/TIIS2019.pdf},
year = {2018},
date = {2018-01-01},
journal = {ACM Transactions on Interactive Intelligent Systems},
pages = {1-37},
abstract = {Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory
search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error
and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces
with increased user control have been developed to support exploratory search process. In this work we
present our attempt to increase the power of exploratory search interfaces by using ideas of social search,
i.e., leveraging information left by past users of information systems. Social search technologies are highly
popular nowadays, especially for improving ranking. However, current approaches to social ranking do not
allow users to decide to what extent social information should be taken into account for result ranking. This
paper presents an interface that integrates social search functionality into an exploratory search system in
a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates
control features that allow the user to (i) express information needs by selecting keywords and (ii) to express
preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes
search transparency through color-coded stacked bars and rich tooltips. This work presents the full series
of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user
interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we
investigated system accuracy and subjective aspects with a structural model that revealed that, when users
actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only
tool and users were more satis"ed.},
note = {Pre SFI},
keywords = {CCS Concepts, Computer systems organization, Embedded systems, Network reliability;, Networks, Redundancy, Robotics},
pubstate = {published},
tppubtype = {article}
}
search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error
and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces
with increased user control have been developed to support exploratory search process. In this work we
present our attempt to increase the power of exploratory search interfaces by using ideas of social search,
i.e., leveraging information left by past users of information systems. Social search technologies are highly
popular nowadays, especially for improving ranking. However, current approaches to social ranking do not
allow users to decide to what extent social information should be taken into account for result ranking. This
paper presents an interface that integrates social search functionality into an exploratory search system in
a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates
control features that allow the user to (i) express information needs by selecting keywords and (ii) to express
preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes
search transparency through color-coded stacked bars and rich tooltips. This work presents the full series
of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user
interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we
investigated system accuracy and subjective aspects with a structural model that revealed that, when users
actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only
tool and users were more satis"ed.
2016
Belgin Mutlu; Eduardo Veas; Christoph Trattner
VizRec: Recommending Personalized Visualizations Journal Article
In: ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 6, no. 4, pp. 1-40, 2016, (Pre SFI).
Abstract | BibTeX | Tags: CCS Concepts, Collaborative filtering;, Content ranking, Human-centered computing, Information Systems, Personalization | Links:
@article{Mutlu2016,
title = {VizRec: Recommending Personalized Visualizations},
author = {Belgin Mutlu and Eduardo Veas and Christoph Trattner},
url = {https://www.christophtrattner.info/pubs/ACM-TIIS.pdf},
year = {2016},
date = {2016-01-01},
journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},
volume = {6},
number = {4},
pages = {1-40},
abstract = {Visualizations have a distinctive advantage when dealing with the information overload problem: since they
are grounded in basic visual cognition, many people understand them. However, creating the appropriate
representation requires specific expertise of the domain and underlying data. Our quest in this paper is to
study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has
to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a
visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects
of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can
we capture and use those aspects to recommend visualizations? This paper investigates strategies to
recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to
estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts
are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines
information on what a chart is about (tags) and how good it is (ratings). We present the design principles
behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a
crowd sourced study, and the analysis of strategies for visualization recommendation.},
note = {Pre SFI},
keywords = {CCS Concepts, Collaborative filtering;, Content ranking, Human-centered computing, Information Systems, Personalization},
pubstate = {published},
tppubtype = {article}
}
are grounded in basic visual cognition, many people understand them. However, creating the appropriate
representation requires specific expertise of the domain and underlying data. Our quest in this paper is to
study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has
to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a
visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects
of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can
we capture and use those aspects to recommend visualizations? This paper investigates strategies to
recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to
estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts
are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines
information on what a chart is about (tags) and how good it is (ratings). We present the design principles
behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a
crowd sourced study, and the analysis of strategies for visualization recommendation.