Towards Responsible Media Recommendation

Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.publishedVersio

Real-time annotation of video streams using staged processing

Real-time media rich applications rely on live streams of rich and accurate meta-data describing the video content to provide personal user experiences. Unfortunately, the general amount of video meta-data today is often limited to titles, synopsis and a few keywords. A wildly used approach for extraction of meta-data from video is computer vision. It has been developed a number of different video processing algorithms which can analyse and retrieve useful data from video. However, the computational cost of current computer vision algorithms is considerable. This thesis presents a software architecture that aims to enable real-time annotation of multiple live video streams. The architecture is intended for use within media rich applications where extraction of video semantics in real-time is necessary. Our conjecture was that staging video processing in levels will make room for a more scalable video annotation system. To evaluate our thesis we have developed the prototype runtime Árvdadus. Our experiments show that staged processing can decrease the computation time of meta-data extraction. The evaluation of the architecture suggests that the architecture is applicable in a wide range of domains where extraction of meta-data in real-time is necessar

NFC Prototype Bonanza

This report presents the results of the research conducted by Tor Kreutzer and Ø yvind Holmstad during the summer of 2011 at the University of Troms ø. The goal of the project was to gain practical experience with Near Field Communication (NFC) technology by exploring its properties through hands-on development of applications and services. To explore the applicability and limitations of NFC we have developed a range of diff erent prototype applications and services that utilize the technology in di fferent ways. The applications are mainly implemented on the Android mobile platform, but many of them communicate with servers running on traditional computers. The applications vary widely, from the simple Tagger which can read and write NFC tags, to NFC Presenter which uses NFC to simplify the process of starting presentations from your mobile device. NFC Safari uses the user's location to identify the closest sightseeing spot, and from there on takes him on a city safari. Applications like PartyShare and Are You the One? explores how powerful NFC can be in a social setting. Detailed descriptions and implementation details of all applications can be found in chapter 4

Towards Responsible Media Recommendation

Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future

Towards Responsible Media Recommendation

Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future