@article{Trattner2020,
title = {Learning to Recommend Similar Items from Human Judgements},
author = {Christoph Trattner and Dietmar Jannach},
url = {https://www.christophtrattner.info/pubs/UMUAI2019.pdf},
doi = {10.1007/s11257-019-09245-4},
year = {2019},
date = {2019-09-20},
journal = {User Modeling and User-Adapted Interaction Journal},
pages = {1-50},
abstract = {Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@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.},
keywords = {},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}