@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}