@conference{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
https://figshare.com/articles/dataset/MVT9KD/14528727/1?file=27853566 },
doi = {10.1145/3450614.3461687},
year = {2021},
date = {2021-06-01},
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 = {conference}
}

@conference{Ljungblad2021,
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 = {Sara Ljungblad and Yemao Man and Mehmet Aydın Baytaş and Mafalda Gamboa and Mohammad Obaid and Morten Fjeld},
url = {https://dl.acm.org/doi/pdf/10.1145/3411764.3445737},
doi = {https://doi.org/10.1145/3411764.3445737},
year = {2021},
date = {2021-05-08},
number = {Article 159},
pages = {1-16},
publisher = {Association for Computing Machinery, New York, NY, USA},
organization = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21)},
abstract = {Human-drone interaction is a growing topic of interest within HCI research. Researchers propose many innovative concepts for drone applications, but much of this research does not incorporate knowledge on existing applications already adopted by professionals. This limits the validity of said research. To address this limitation, we present our findings from an in-depth interview study with 10 professional drone pilots. Our participants were armed with significant experience and qualifications – pertinent to both drone operations and a set of applications covering diverse industries. Our findings have resulted in design recommendations that should inform both ends and means of human-drone interaction research. These include, but are not limited to: safety-related protocols, insights from domain-specific use cases, and relevant practices outside of hands-on flight.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}