2022
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Hybrid Recommendation of Movies based on Deep Content Features Inproceedings Tord Kvifte; Mehdi Elahi; Christoph Trattner In: Springer Nature, 2022. @inproceedings{cristin1957037,
title = {Hybrid Recommendation of Movies based on Deep Content Features},
author = {Tord Kvifte and Mehdi Elahi and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957037, Cristin
https://aip-research-center.github.io/AIPA_workshop/2021/},
year = {2022},
date = {2022-01-01},
booktitle = {Springer Nature},
abstract = {When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. |
Service-Oriented Computing - ICSOC 2021 Workshops - AIOps, STRAPS, AI-PA, and Satellite Events, Dubai, United Arab Emirates, November 22-25, 2021, Proceedings. Lecture Notes in Computer Science. Proceeding Hakim Hacid; Monther Aldwairi; Mohamed Reda Bouadjenek; Marinella Petrocchi; Noura Faci; Fatma Outay; Amin Beheshti; Lauritz Thamsen; Hai Dong 2022. @proceedings{cristin1957036,
title = {Service-Oriented Computing - ICSOC 2021 Workshops - AIOps, STRAPS, AI-PA, and Satellite Events, Dubai, United Arab Emirates, November 22-25, 2021, Proceedings. Lecture Notes in Computer Science.},
author = {Hakim Hacid and Monther Aldwairi and Mohamed Reda Bouadjenek and Marinella Petrocchi and Noura Faci and Fatma Outay and Amin Beheshti and Lauritz Thamsen and Hai Dong},
url = {https://app.cristin.no/results/show.jsf?id=1957036, Cristin
https://link.springer.com/conference/icsoc},
year = {2022},
date = {2022-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {proceedings}
}
|
2021
|
Responsible media technology and AI: challenges and research directions Journal Article Christoph Trattner; Dietmar Jannach; Enrico Motta; Irene Costera Meijer; Nicholas Diakopoulos; Mehdi Elahi; Andreas Lothe Opdahl; Bjørnar Tessem; Njål Trygve Borch; Morten Fjeld; Lilja Øvrelid; Koenraad De Smedt; Hallvard Moe In: AI and Ethics, 2021. @article{cristin2000622,
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 Trygve Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin
https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},
doi = {https://doi.org/10.1007/s43681-021-00126-4},
year = {2021},
date = {2021-12-20},
journal = {AI and Ethics},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
Towards Responsible Media Recommendation Journal Article Mehdi Elahi; Dietmar Jannach; Lars Skjærven; Erik Knudsen; Helle Sjøvaag; Kristian Tolonen; Øyvind Holmstad; Igor Pipkin; Eivind Throndsen; Agnes Stenbom; Eivind Fiskerud; Adrian Oesch; Loek Vredenberg; Christoph Trattner In: AI and Ethics, 2021. @article{cristin1953352,
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://app.cristin.no/results/show.jsf?id=1953352, Cristin
https://link.springer.com/article/10.1007%2Fs43681-021-00107-7},
doi = {https://doi.org/10.1007/s43681-021-00107-7},
year = {2021},
date = {2021-11-02},
journal = {AI and Ethics},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
Når kunstig intelligens inntar redaksjonen Medium Bjørnar Tessem 2021. @media{cristin1942282,
title = {Når kunstig intelligens inntar redaksjonen},
author = {Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1942282, Cristin},
year = {2021},
date = {2021-10-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
Beyond Algorithmic Fairness in Recommender Systems Inproceedings Mehdi Elahi; Himan Abdollahpouri; Masoud Mansoury; Helma Torkamaan In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956964,
title = {Beyond Algorithmic Fairness in Recommender Systems},
author = {Mehdi Elahi and Himan Abdollahpouri and Masoud Mansoury and Helma Torkamaan},
url = {https://app.cristin.no/results/show.jsf?id=1956964, Cristin
https://dl.acm.org/doi/abs/10.1145/3450614.3461685},
doi = {https://doi.org/https://doi.org/10.1145/3450614.3461685},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Recommending Videos in Cold Start With Automatic Visual Tags Inproceedings Mehdi Elahi; Farshad Bakhshandegan Moghaddam; Reza Hosseini; Mohammad Hossein Rimaz; Nabil El Ioini; Marko Tkalcic; Christoph Trattner; Tammam Tillo In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956967,
title = {Recommending Videos in Cold Start With Automatic Visual Tags},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Nabil El Ioini and Marko Tkalcic and Christoph Trattner and Tammam Tillo},
url = {https://app.cristin.no/results/show.jsf?id=1956967, Cristin
https://dl.acm.org/doi/10.1145/3450614.3461687},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
abstract = {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 = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Inproceedings Alain Dominique Starke; Edis Asotic; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956504,
title = {“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface},
author = {Alain Dominique Starke and Edis Asotic and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956504, Cristin},
doi = {https://doi.org/10.1145/3460231.3474232},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems Inproceedings Alain Dominique Starke; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956555,
title = {Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems},
author = {Alain Dominique Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956555, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Exploring the effects of natural language justifications on food recommender systems Inproceedings Cataldo Musto; Alain Dominique Starke; Christoph Trattner; Amon Rapp; Giovanni Semeraro In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956541,
title = {Exploring the effects of natural language justifications on food recommender systems},
author = {Cataldo Musto and Alain Dominique Starke and Christoph Trattner and Amon Rapp and Giovanni Semeraro},
url = {https://app.cristin.no/results/show.jsf?id=1956541, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric Inproceedings Alain Dominique Starke; Christoph Trattner; Hedda Bakken; Martin Skivenesvåg Johannessen; Vegard Solberg In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956600,
title = {The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric},
author = {Alain Dominique Starke and Christoph Trattner and Hedda Bakken and Martin Skivenesvåg Johannessen and Vegard Solberg},
url = {https://app.cristin.no/results/show.jsf?id=1956600, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Developing a Software Reference Architecture for Journalistic Knowledge Platforms Journal Article Marc Gallofré Ocaña; Andreas Lothe Opdahl In: CEUR Workshop Proceedings, 2021. @article{cristin1949655,
title = {Developing a Software Reference Architecture for Journalistic Knowledge Platforms},
author = {Marc Gallofré Ocaña and Andreas Lothe Opdahl},
url = {https://app.cristin.no/results/show.jsf?id=1949655, Cristin
https://ceur-ws.org/Vol-2978/saml-paper2.pdf},
year = {2021},
date = {2021-01-01},
journal = {CEUR Workshop Proceedings},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
Content Analysis and Production Presentation Bjørnar Tessem; Andreas Lothe Opdahl MediaFutures Annual Meeting 2021, 01.01.2021. @misc{cristin1942264,
title = {Content Analysis and Production},
author = {Bjørnar Tessem and Andreas Lothe Opdahl},
url = {https://app.cristin.no/results/show.jsf?id=1942264, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {MediaFutures Annual Meeting 2021},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
Investigating the impact of recommender systems on user-based and item-based popularity bias Journal Article Mehdi Elahi; Danial Khosh Kholgh; Mohammad Sina Kiarostami; Sorush Saghari; Shiva Parsa Rad; Marko Tkalcic In: Information Processing & Management, 2021. @article{cristin1953363,
title = {Investigating the impact of recommender systems on user-based and item-based popularity bias},
author = {Mehdi Elahi and Danial Khosh Kholgh and Mohammad Sina Kiarostami and Sorush Saghari and Shiva Parsa Rad and Marko Tkalcic},
url = {https://app.cristin.no/results/show.jsf?id=1953363, Cristin
https://www.sciencedirect.com/science/article/pii/S0306457321001436},
doi = {https://doi.org/10.1016/j.ipm.2021.102655},
year = {2021},
date = {2021-01-01},
journal = {Information Processing & Management},
abstract = {Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.
In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.
The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.
In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.
The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages. |
Changing Salty Food Preferences with Visual and Textual
Explanations in a Search Interface Journal Article Arngeir Berge; Vegard Velle Sjøen; Alain Starke; Christoph Trattner In: CEUR Workshop Proceedings, 2021. @article{cristin1933059,
title = {Changing Salty Food Preferences with Visual and Textual
Explanations in a Search Interface},
author = {Arngeir Berge and Vegard Velle Sjøen and Alain Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1933059, Cristin
https://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-2.pdf},
year = {2021},
date = {2021-01-01},
journal = {CEUR Workshop Proceedings},
abstract = {Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste. |
Contagious "Corona" Compounding by Journalists in a CLARIN Newspaper Monitor Corpus Inproceedings Koenraad De Smedt In: 2021. @inproceedings{cristin1918658,
title = {Contagious "Corona" Compounding by Journalists in a CLARIN Newspaper Monitor Corpus},
author = {Koenraad De Smedt},
url = {https://app.cristin.no/results/show.jsf?id=1918658, Cristin
https://ecp.ep.liu.se/index.php/clarin/article/view/10/209},
doi = {https://doi.org/https://doi.org/10.3384/ecp18010},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
How the public understands news media trust: An open-ended approach Journal Article Erik Knudsen; Stefan Dahlberg; Magnus Hoem Iversen; Mikael Poul Johannesson; Silje Nygaard In: Journalism - Theory, Practice & Criticism, 2021. @article{cristin1902285,
title = {How the public understands news media trust: An open-ended approach},
author = {Erik Knudsen and Stefan Dahlberg and Magnus Hoem Iversen and Mikael Poul Johannesson and Silje Nygaard},
url = {https://app.cristin.no/results/show.jsf?id=1902285, Cristin},
doi = {https://doi.org/10.1177/14648849211005892},
year = {2021},
date = {2021-01-01},
journal = {Journalism - Theory, Practice & Criticism},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
Using Gender- and Polarity-Informed Models to Investigate Bias Inproceedings Samia Touileb; Lilja Øvrelid; Erik Velldal In: Association for Computational Linguistics, 2021. @inproceedings{cristin1924816,
title = {Using Gender- and Polarity-Informed Models to Investigate Bias},
author = {Samia Touileb and Lilja Øvrelid and Erik Velldal},
url = {https://app.cristin.no/results/show.jsf?id=1924816, Cristin
https://aclanthology.org/2021.gebnlp-1.8/},
doi = {https://doi.org/10.18653/v1/2021.gebnlp-1.8},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computational Linguistics},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface Inproceedings Arngeir Berge; Vegard Velle Sjøen; Alain Dominique Starke; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956563,
title = {Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface},
author = {Arngeir Berge and Vegard Velle Sjøen and Alain Dominique Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956563, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Predicting Feature-based Similarity in the News Domain Using Human Judgments Inproceedings Alain Dominique Starke; Sebastian Øverhaug Larsen; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956594,
title = {Predicting Feature-based Similarity in the News Domain Using Human Judgments},
author = {Alain Dominique Starke and Sebastian Øverhaug Larsen and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956594, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
MORS 2021: 1st Workshop on Multi Objective Recommender Systems Inproceedings Himan Abdollahpouri; Mehdi Elahi; Masoud Mansoury; Shaghayegh Sahebi; Zahra Nazari; Allison Chaney; Babak Loni In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956978,
title = {MORS 2021: 1st Workshop on Multi Objective Recommender Systems},
author = {Himan Abdollahpouri and Mehdi Elahi and Masoud Mansoury and Shaghayegh Sahebi and Zahra Nazari and Allison Chaney and Babak Loni},
url = {https://app.cristin.no/results/show.jsf?id=1956978, Cristin
https://dl.acm.org/doi/10.1145/3460231.3470936},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
abstract = {Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop. |
The Effect of the COVID-19 Pandemic Crisis on Trust in the News Media: Evidence From Three Panel Waves With a Pre-Crisis Baseline Erik Knudsen Presentation Erik Knudsen; Åsta Dyrnes Nordø; Magnus Hoem Iversen 71st Annual ICA Conference, 01.01.2021. @misc{cristin1957204,
title = {The Effect of the COVID-19 Pandemic Crisis on Trust in the News Media: Evidence From Three Panel Waves With a Pre-Crisis Baseline Erik Knudsen},
author = {Erik Knudsen and Åsta Dyrnes Nordø and Magnus Hoem Iversen},
url = {https://app.cristin.no/results/show.jsf?id=1957204, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {71st Annual ICA Conference},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
Ting i internettet Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1931531,
title = {Ting i internettet},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1931531, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
Automatiske nyhende Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1905817,
title = {Automatiske nyhende},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1905817, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
EN ANALYSE AV SAMMENHENGEN MELLOM BRUK AV NRKS DIGITALE NYHETSTILBUD OG BETALINGSVILJE FOR DIGITALE NYHETER Technical Report Erik Knudsen; Hallvard Moe 2021. @techreport{cristin1958452,
title = {EN ANALYSE AV SAMMENHENGEN MELLOM BRUK AV NRKS DIGITALE NYHETSTILBUD OG BETALINGSVILJE FOR DIGITALE NYHETER},
author = {Erik Knudsen and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=1958452, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {techreport}
}
|
Using Gender- and Polarity-informed Models to Investigate Bias Working paper Samia Touileb; Lilja Øvrelid; Erik Velldal 2021. @workingpaper{cristin1958571,
title = {Using Gender- and Polarity-informed Models to Investigate Bias},
author = {Samia Touileb and Lilja Øvrelid and Erik Velldal},
url = {https://app.cristin.no/results/show.jsf?id=1958571, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Novel Methods Using Human Emotion and Visual Features for Recommending Movies Masters Thesis Mehdi Elahi; Øyvind Johannessen Universitetet i Bergen, 2021. @mastersthesis{cristin1957008,
title = {Novel Methods Using Human Emotion and Visual Features for Recommending Movies},
author = {Mehdi Elahi and Øyvind Johannessen},
url = {https://app.cristin.no/results/show.jsf?id=1957008, Cristin},
year = {2021},
date = {2021-01-01},
school = {Universitetet i Bergen},
abstract = {This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of Accuracy, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of Diversity, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that joy and disgust tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Emotion-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {mastersthesis}
}
This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of Accuracy, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of Diversity, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that joy and disgust tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Emotion-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies. |
Unify Media and UX with timed variables Working paper Ingar M Arntzen; Njål Trygve Borch; Anders Andersen 2021. @workingpaper{cristin1959749,
title = {Unify Media and UX with timed variables},
author = {Ingar M Arntzen and Njål Trygve Borch and Anders Andersen},
url = {https://app.cristin.no/results/show.jsf?id=1959749, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Video Recommendations Based on Visual Features Extracted with Deep Learning Masters Thesis Mehdi Elahi; Tord Kvifte Universitetet i Bergen, 2021. @mastersthesis{cristin1956990,
title = {Video Recommendations Based on Visual Features Extracted with Deep Learning},
author = {Mehdi Elahi and Tord Kvifte},
url = {https://app.cristin.no/results/show.jsf?id=1956990, Cristin
https://hdl.handle.net/11250/2760300},
year = {2021},
date = {2021-01-01},
school = {Universitetet i Bergen},
abstract = {When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {mastersthesis}
}
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository. |
Beyond Algorithmic Fairness in Recommender System Working paper Mehdi Elahi; Himan Abdollahpouri; Masoud Mansoury; Helma Torkamaan 2021. @workingpaper{cristin1957035,
title = {Beyond Algorithmic Fairness in Recommender System},
author = {Mehdi Elahi and Himan Abdollahpouri and Masoud Mansoury and Helma Torkamaan},
url = {https://app.cristin.no/results/show.jsf?id=1957035, Cristin
https://dl.acm.org/doi/abs/10.1145/3450614.3461685},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Enhanced Movie Recommendation Incorporating Visual Features Working paper Mehdi Elahi; Farshad Bakhshandegan Moghaddam; Reza Hosseini; Mohammad Hossein Rimaz; Christoph Trattner 2021. @workingpaper{cristin1957034,
title = {Enhanced Movie Recommendation Incorporating Visual Features},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957034, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Medieundersøkelsen 2021: Har koronadekningen svekket tilliten til mediene? Presentation Erik Knudsen Nordiske Mediedager 2021, 01.01.2021. @misc{cristin1957213,
title = {Medieundersøkelsen 2021: Har koronadekningen svekket tilliten til mediene?},
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957213, Cristin
https://www.nordiskemediedager.no/medieundersoekelsen/medieundersoekelsen/},
year = {2021},
date = {2021-01-01},
howpublished = {Nordiske Mediedager 2021},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
Auka røynd (AR) Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1894361,
title = {Auka røynd (AR)},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1894361, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
The Promise and Perils of Algorithmic News Recommenders' Influence on Democracy Presentation Erik Knudsen TEDxBergen2021, 01.01.2021. @misc{cristin1957207,
title = {The Promise and Perils of Algorithmic News Recommenders' Influence on Democracy},
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957207, Cristin
https://youtu.be/B3qHJC5LwJQ},
year = {2021},
date = {2021-01-01},
howpublished = {TEDxBergen2021},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
How can news sites personalize audiences's news experiences without making audiences more polarized and fragmented? Working paper Erik Knudsen 2021. @workingpaper{cristin1957727,
title = {How can news sites personalize audiences's news experiences without making audiences more polarized and fragmented? },
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957727, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
What can TV companies do for teenagers who are on their phone all the time? Working paper John Magnus Ragnhildson Dahl 2021. @workingpaper{cristin1957717,
title = {What can TV companies do for teenagers who are on their phone all the time?},
author = {John Magnus Ragnhildson Dahl},
url = {https://app.cristin.no/results/show.jsf?id=1957717, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Datafication, Media and Democracy: Transformation of news work in datafied society Working paper Ana Milojevic 2021. @workingpaper{cristin1957739,
title = {Datafication, Media and Democracy: Transformation of news work in datafied society},
author = {Ana Milojevic},
url = {https://app.cristin.no/results/show.jsf?id=1957739, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Why won't young people pay for news? Working paper Marianne Borchgrevink-Brækhus 2021. @workingpaper{cristin1957735,
title = {Why won't young people pay for news?},
author = {Marianne Borchgrevink-Brækhus},
url = {https://app.cristin.no/results/show.jsf?id=1957735, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
Kva datamaskiner kan gjere Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1957728,
title = {Kva datamaskiner kan gjere},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1957728, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
Fake news og mediebruk Presentation Hallvard Moe Vestlandsseminaret, 01.01.2021. @misc{cristin1957718,
title = {Fake news og mediebruk},
author = {Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=1957718, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Vestlandsseminaret},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research Proceeding Sarah Ljungblad; Yemao Man; Mehmet Aydın Baytaş; Mafalda Gamboa; Morten Fjeld; Mohammad Obaid ACM CHI on human factors in computing systems conference proceeding, 2021. @proceedings{cristin2003885,
title = {What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research},
author = {Sarah Ljungblad and Yemao Man and Mehmet Aydın Baytaş and Mafalda Gamboa and Morten Fjeld and Mohammad Obaid},
url = {https://app.cristin.no/results/show.jsf?id=2003885, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {ACM CHI on human factors in computing systems conference proceeding},
keywords = {Cristin},
pubstate = {published},
tppubtype = {proceedings}
}
|
Kvifor Google-briller vart ein fiasko Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1942262,
title = {Kvifor Google-briller vart ein fiasko},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1942262, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|