TY - CHAP A1 - Kauer, Tobias A1 - Joglekar, Sagar A1 - Redi, Miriam A1 - Aiello, Luca Maria A1 - Quercia, Daniele T1 - Mapping and Visualizing Deep-Learning Urban Beautification T2 - IEEE Computer Graphics and Applications N2 - Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience. KW - information visualization KW - data and knowledge visualization KW - urban informatics KW - Visualisierung KW - Deep learning KW - Computergrafik KW - Umfrage Y1 - 2018 U6 - https://doi.org/10.1109/MCG.2018.053491732 SN - 1558-1756 VL - 38 IS - 5 SP - 70 EP - 83 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER - TY - CHAP A1 - Said, Alan A1 - De Luca, Ernesto William A1 - Quercia, Daniele T1 - Report on the 4th Workshop on Context-awareness in Retrieval and Recommendation (CaRR 2014) T2 - ACM SIGIR Forum N2 - Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user's current situation and interests. The workshop on Context-awareness in Retrieval and Recommendation is a forum for research on context-awareness in information retrieval, recommender systems and human computer interaction. The fourth iteration of the workshop was organized in conjunction to the 36th European Conference on Information Retrieval in Amsterdam, The Netherlands. KW - Information Retrieval KW - Empfehlungssystem Y1 - 2014 U6 - https://doi.org/10.1145/2701583.2701595 SN - 0163-5840 VL - 48 IS - 2 SP - 89 EP - 92 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Said, Alan A1 - De Luca, Ernesto William A1 - Quercia, Daniele A1 - Böhmer, Matthias T1 - 4th Workshop on Context-Awareness in Retrieval and Recommendation T2 - Advances in Information Retrieval N2 - Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user’s current situation and interests. Ubiquitous computing further provides new means for capturing user feedback on items and providing information. KW - Information Retrieval KW - Empfehlungssystem KW - Ubiquitous Computing Y1 - 2014 SN - 978-3-319-06028-6 U6 - https://doi.org/10.1007/978-3-319-06028-6_100 SN - 1611-3349 SP - 802 EP - 805 PB - Springer CY - Cham ER -