2021
|
| Responsible media technology and AI: challenges and research directions Journal Article Trattner, Christoph; Jannach, Dietmar; Motta, Enrico; Meijer, Irene Costera; Diakopoulos, Nicholas; Elahi, Mehdi; Opdahl, Andreas Lothe; Tessem, Bjørnar; Borch, Njål; Fjeld, Morten; Øvrelid, Lilja; Smedt, Koenraad De; Moe, Hallvard AI and Ethics , 2021. Abstract | Links | BibTeX @article{Trattner2021,
title = {Responsible media technology and AI: challenges and research directions},
author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas Lothe Opdahl and Bjørnar Tessem and Njål Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},
url = {https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},
doi = {10.1007/s43681-021-00126-4},
year = {2021},
date = {2021-12-20},
journal = {AI and Ethics },
abstract = {The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed. |
| Towards Responsible Media Recommendation Journal Article Elahi, Mehdi; Jannach, Dietmar; Skjærven, Lars; Knudsen, Erik; Sjøvaag, Helle; Tolonen, Kristian; Holmstad, Øyvind; Pipkin, Igor; Throndsen, Eivind; Stenbom, Agnes; Fiskerud, Eivind; Oesch, Adrian; Vredenberg, Loek; Trattner, Christoph AI and Ethics , 2021. Links | BibTeX @article{Elahi2021bb,
title = {Towards Responsible Media Recommendation},
author = {Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Erik Knudsen and Helle Sjøvaag and Kristian Tolonen and Øyvind Holmstad and Igor Pipkin and Eivind Throndsen and Agnes Stenbom and Eivind Fiskerud and Adrian Oesch and Loek Vredenberg and Christoph Trattner},
url = {https://mediafutures.no/elahi2021_article_towardsresponsiblemediarecomme-2/},
doi = {https://doi.org/10.1007/s43681-021-00107-7},
year = {2021},
date = {2021-11-02},
journal = {AI and Ethics },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric. Proceeding Forthcoming Starke, Alain; Trattner, Christoph; Bakken, H; Johannessen, M; Solberg, V Forthcoming. BibTeX @proceedings{Starke2021bb,
title = {The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric.},
author = {Alain Starke and Christoph Trattner and H. Bakken and M. Johannessen and V. Solberg},
year = {2021},
date = {2021-09-27},
journal = {Workshop on Multi-Objective Recommender Systems (MORS) held at RecSys'21. },
keywords = {},
pubstate = {forthcoming},
tppubtype = {proceedings}
}
|
| Recommending Videos in Cold Start With Automatic Visual Tags Proceeding Elahi, Mehdi; Moghaddam, Farshad B; Hosseini, Reza; Rimaz, Mohammad Hossein; Ioini, Nabil El; Tkalcic, Marko; Trattner, Christoph; Tillo, Tammam 29th Conference on User Modeling, Adaptation and Personalization conference (UMAP 2021) Utrecht, the Netherlands, 2021. Abstract | Links | BibTeX @proceedings{Elahi2021,
title = {Recommending Videos in Cold Start With Automatic Visual Tags},
author = {Mehdi Elahi and Farshad B. Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Nabil El Ioini and Marko Tkalcic and Christoph Trattner and Tammam Tillo },
url = {https://www.researchgate.net/publication/351696161_Recommending_Videos_in_Cold_Start_With_Automatic_Visual_Tags
},
doi = {10.1145/3450614.3461687},
year = {2021},
date = {2021-07-01},
address = {Utrecht, the Netherlands},
organization = {29th Conference on User Modeling, Adaptation and Personalization conference (UMAP 2021)},
abstract = {Preprint and dataset available! This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Preprint and dataset available! This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags. |
| Exploring the Effects of Natural Language Justifications in Food Recommender Systems Proceeding Musto, Cataldo; Starke, Alain D; Trattner, Christoph; Rapp, Amon; Semeraro, Giovanni Association for Computing Machinery, New York, NY United States, 2021, ISBN: 978-1-4503-8366-0. Links | BibTeX @proceedings{Semeraro2021,
title = {Exploring the Effects of Natural Language Justifications in Food Recommender Systems},
author = {Cataldo Musto and Alain D. Starke and Christoph Trattner and Amon Rapp and Giovanni Semeraro
},
editor = {Judith Masthoff; Eelco Herder; Nava Tintarev; Marko Tkalčič},
doi = {https://doi.org/10.1145/3450613.3456827},
isbn = {978-1-4503-8366-0},
year = {2021},
date = {2021-06-21},
booktitle = {UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization },
pages = {147–157},
publisher = {Association for Computing Machinery},
address = {New York, NY United States},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
| Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article Starke, Alain D; Willemsen, Martijn C; Trattner, Christoph (April 2021), pp. 1-18, 2021. Abstract | Links | BibTeX @article{Starke2021,
title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},
author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},
doi = {10.3389/frai.2021.621743},
year = {2021},
date = {2021-04-22},
number = {April 2021},
pages = {1-18},
abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction |
| Exploring Multi-List User Interfaces for Similar-Item Recommendations Conference Jannach, Dietmar; Jesse, Mathias; Jugovac, Michael; Trattner, Christoph 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21) 2021. Abstract | Links | BibTeX @conference{Jannach2021,
title = {Exploring Multi-List User Interfaces for Similar-Item Recommendations},
author = {Dietmar Jannach and Mathias Jesse and Michael Jugovac and Christoph Trattner},
url = {https://mediafutures.no/conference_umap_2021-2/},
year = {2021},
date = {2021-03-26},
organization = {29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21)},
abstract = {On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures. |
2019
|
| Learning to Recommend Similar Items from Human Judgements Journal Article Trattner, Christoph; Jannach, Dietmar User Modeling and User-Adapted Interaction Journal, pp. 1-50, 2019, (Pre SFI). Abstract | Links | BibTeX @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.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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. |
2018
|
| Towards a big data platform for news angles Workshop Ocaña, Marc Gallofré; Nyre, Lars; Opdahl, Andreas Lothe; Tessem, Bjørnar; Trattner, Christoph; Veres, Csaba Norwegian Big Data Symposium 2018, 2018, (Pre SFI). Abstract | Links | BibTeX @workshop{Ocaña2018,
title = {Towards a big data platform for news angles},
author = {Marc Gallofré Ocaña and Lars Nyre and Andreas Lothe Opdahl and Bjørnar Tessem and Christoph Trattner and Csaba Veres},
url = {https://www.researchgate.net/publication/332274562_Towards_a_Big_Data_Platform_for_News_Angles},
year = {2018},
date = {2018-11-01},
booktitle = {Norwegian Big Data Symposium 2018},
abstract = {Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
|
| The Roadmap to User-Controllable Social Exploratory Search Journal Article Sciascio, Cecilia Di; Brusilovsky, Peter; Trattner, Christoph; Veas, Eduardo ACM Transactions on Interactive Intelligent Systems, pp. 1-37, 2018, (Pre SFI). Abstract | Links | BibTeX @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 = {},
pubstate = {published},
tppubtype = {article}
}
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. |
2017
|
| Exploiting Food Choice Biases for Healthier Recipe Recommendation Conference Elsweiler, David; Trattner, Christoph; Harvey, Morgan ACM SIGIR Conference 2017, (Pre SFI). Abstract | Links | BibTeX @conference{Elsweiler2017,
title = {Exploiting Food Choice Biases for Healthier Recipe Recommendation},
author = {David Elsweiler and Christoph Trattner and Morgan Harvey},
url = {https://www.christophtrattner.info/pubs/SIGIR2017.pdf},
year = {2017},
date = {2017-08-01},
organization = {ACM SIGIR Conference},
abstract = {By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have di erent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have di erent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems. |
| Visually-Aware Video Recommendation in the Cold Start Conference Elahi, Mehdi; Hosseini, Reza; Rimaz, Mohammad H; Moghaddam, Farshad B; Trattner, Christoph Proccedings of theACM Hypertext 2020 2017, (Pre SFI). Abstract | Links | BibTeX @conference{Elahi2017b,
title = {Visually-Aware Video Recommendation in the Cold Start},
author = {Mehdi Elahi and Reza Hosseini and Mohammad H. Rimaz and Farshad B. Moghaddam and Christoph Trattner},
url = {https://christophtrattner.com/pubs/ht2020.pdf},
year = {2017},
date = {2017-07-01},
pages = {1-5},
organization = {Proccedings of theACM Hypertext 2020},
abstract = {Recommender Systems (RSs) have become essential tools in any
video-sharing platforms (such as YouTube) by generating video
suggestions for users. Although, RSs have been e!ective, however,
they su!er from the so-called New Item problem. New item problem,
as part of Cold Start problem, happens when a new item is added to
the system catalogue and the RS has no or little data available for
that new item. In such a case, the system may fail to meaningfully
recommend the new item to any user.
In this paper, we propose a novel recommendation technique
based on visual tags, i.e., tags that are automatically annotated
to videos based on visual description of videos. Such visual tags
can be used in an extreme cold start situation, where neither any
rating, nor any tag is available for the new video item. The visual
tags could also be used in the moderate cold start situation when
the new video item has been annotated with few tags. This type
of content features can be extracted automatically without any
human involvement and have been shown to be very e!ective in
representing the video content.
We have used a large dataset of videos and shown that automatically
extracted visual tags can be incorporated into the cold start
recommendation process and achieve superior results compared to
the recommendation based on human-annotated tags.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Recommender Systems (RSs) have become essential tools in any
video-sharing platforms (such as YouTube) by generating video
suggestions for users. Although, RSs have been e!ective, however,
they su!er from the so-called New Item problem. New item problem,
as part of Cold Start problem, happens when a new item is added to
the system catalogue and the RS has no or little data available for
that new item. In such a case, the system may fail to meaningfully
recommend the new item to any user.
In this paper, we propose a novel recommendation technique
based on visual tags, i.e., tags that are automatically annotated
to videos based on visual description of videos. Such visual tags
can be used in an extreme cold start situation, where neither any
rating, nor any tag is available for the new video item. The visual
tags could also be used in the moderate cold start situation when
the new video item has been annotated with few tags. This type
of content features can be extracted automatically without any
human involvement and have been shown to be very e!ective in
representing the video content.
We have used a large dataset of videos and shown that automatically
extracted visual tags can be incorporated into the cold start
recommendation process and achieve superior results compared to
the recommendation based on human-annotated tags. |
2016
|
| VizRec: Recommending Personalized Visualizations Journal Article Mutlu, Belgin; Veas, Eduardo; Trattner, Christoph ACM Transactions on Interactive Intelligent Systems (TiiS), 6 (4), pp. 1-40, 2016, (Pre SFI). Abstract | Links | BibTeX @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 = {},
pubstate = {published},
tppubtype = {article}
}
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. |
