The Personalization team makes deciding what to play next easier and more enjoyable for every listener. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. Spotify is looking for a Senior Data Engineer to join the team! You will build data driven solutions to bring music and digital media experiences to hundreds of millions of active users and millions of creators by matching fans with creators in a personal and relevant way.

"Made for Love," which is now streaming on HBO Max, opens on a vast expanse of desert, empty save for a geometric building in the distance. A lid on the ground is unlatched, and out pops a woman in a sequinned dress, gasping for breath, her hair drenched with water and a little blood. The woman is Hazel Green, and she is portrayed by Cristin Milioti, a strongly expressive actor who has become known for deploying her feral intellect to outsmart male villains in science-fiction thrillers. If you have seen Milioti take down a video-game dictator in the "Black Mirror" episode "USS Callister," or hack a time-loop purgatory in the 2020 comedy "Palm Springs," then you might be able to guess the story of "Made for Love," even before Hazel raises her middle finger at the structure on the horizon. The place is clearly the source of some terror--one that is futuristic yet eerily familiar.

AI has become a part of all our lives. Our cars have automatic braking, platforms like Netflix and Spotify have recommendations, and Alexa and Google can search things for us on command, all powered by artificial intelligence. Although this technology comes with a lot of convenience and advantages, people are also concerned about its dangers. Inadequate security and ethical problems are a few examples of the cons that come with AI. In response to these dangers, the European Union has decided to work on a legal framework to regulate the way AI is used.

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.

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Machine learning adoption exploded over the past decade, driven in part by the rise of cloud computing, which has made high performance computing and storage more accessible to all businesses. As vendors integrate machine learning into products across industries, and users rely on the output of its algorithms in their decision making, security experts warn of adversarial attacks designed to abuse the technology. Most social networking platforms, online video platforms, large shopping sites, search engines and other services have some sort of recommendation system based on machine learning. The movies and shows that people like on Netflix, the content that people like or share on Facebook, the hashtags and likes on Twitter, the products consumers buy or view on Amazon, the queries users type in Google Search are all fed back into these sites' machine learning models to make better and more accurate recommendations. It's not news that attackers try to influence and skew these recommendation systems by using fake accounts to upvote, downvote, share or promote certain products or content.