@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}

}

@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}

}