Abstract: A machine learning model classifies points of interest in a parallel reality game hosted by a server. The server generates training data sets that include verified properties for points of interest. The machine learning model may predict unverified properties for points of interest. Players in the parallel reality game may input properties for the points of interest. The machine learning model use the received properties from players as inputs to the machine learning model to verify unverified properties or generate new properties for the points of interest. The server may classify the points of interest as suitable for particular activities, and the server may use the classifications for future activities within the parallel reality game.

Abstract: A machine learning model classifies points of interest in a parallel reality game hosted by a server. The server generates training data sets that include verified properties for points of interest. The machine learning model may predict unverified properties for points of interest. Players in the parallel reality game may input properties for the points of interest. The machine learning model use the received properties from players as inputs to the machine learning model to verify unverified properties or generate new properties for the points of interest. The server may classify the points of interest as suitable for particular activities, and the server may use the classifications for future activities within the parallel reality game.

Abstract: A game server generates a parallel-reality game that users may interact with in the real-world. The game server receives user information as users interact with game content. The game server includes a content marketplace that manages the exchange value of additional content items. The content marketplace matches additional game content with users playing the game based on the exchange value of the additional content items and user information. To do so, the content marketplace determines a propensity score for each additional content item quantifying a likelihood the user will interact with the additional content item while interacting with content in the parallel-reality game. The content marketplace provides the additional content item to the user's client device for display in the parallel-reality game based on the propensity score.

Abstract: A machine learning model classifies points of interest in a parallel reality game hosted by a server. The server generates training data sets that include verified properties for points of interest. The machine learning model may predict unverified properties for points of interest. Players in the parallel reality game may input properties for the points of interest. The machine learning model use the received properties from players as inputs to the machine learning model to verify unverified properties or generate new properties for the points of interest. The server may classify the points of interest as suitable for particular activities, and the server may use the classifications for future activities within the parallel reality game.

Abstract: A game server generates a parallel-reality game that users may interact with in the real-world. The game server receives user information as users interact with game content. The game server includes a content marketplace that manages the exchange value of additional content items. The content marketplace matches additional game content with users playing the game based on the exchange value of the additional content items and user information. To do so, the content marketplace determines a propensity score for each additional content item quantifying a likelihood the user will interact with the additional content item while interacting with content in the parallel-reality game. The content marketplace provides the additional content item to the user's client device for display in the parallel-reality game based on the propensity score.