Within Obduction, small spheres of human environments, such as a portion of this house and the land in front of it, have been transported to an alien environment, and players must solve puzzles that mix the human and alien elements. Many objects in the game can be selected and examined in a full three-dimensional view, allowing the player to discover possible hidden drawers or features that aid in puzzle solving. The game features traditional navigation controls for first-person games as well as a node-based point and click system. The game, played out as an adventure game from the first-person perspective, has the player exploring the environment of several different worlds, which includes a mix of alien landscapes and familiar human settings, solving puzzles, and making decisions that can influence how the game ends. In Obduction, the player's character has been abducted from Earth by aliens and transported to one of several alien worlds, with the goal of trying to find a way home. The game was made available for Amazon Luna on October 20, 2020. The Xbox One version of the game was released on April 10, 2020. The PlayStation 4 version was launched in August 2017 with a patch arriving shortly afterwards to support PlayStation VR. A virtual reality version for Oculus Rift system was published in November 2016, while HTC Vive and Oculus Touch versions were released on March 22, 2017. The game was funded via a Kickstarter campaign in October 2013, and was released in August 2016 for Windows-based PCs, with a macOS version released in March 2017. The player must explore and solve puzzles to figure out how to return home. In the game, the player finds their character transported to strange alien worlds but with human elements within the settings. Obduction is considered a spiritual successor to Cyan's previous adventure games, Myst and Riven. Obduction is an adventure video game developed by Cyan Worlds.
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The evaluation metrics used to judge each model include Precision, Recall and Catalog Coverage. Our Factorization Machines model was implemented using LightFM ( ). Our ALS model was implemented through Ben Frederickson's implicit package ( ). We also established a baseline model where the same top-k most popular artists (by total plays) are recommended to each user. The nature of Factorization Machines allowed us to add in user features in the form of the metadata we possess. We implemented and evaluated two main models to recommend artists to Last.fm users, one driven by Alternating Least Squares (ALS) and the other by Factorization Machines. Where possible, we utilized this information as additional features for our more advanced recommender system. We also worked with metadata that contained contextual information for each user, namely age, gender and country. 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