Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to modify a game based on playing style is provided. Player-specific playing style information that indicates one or more attributes of a playing style of a player is accessed. The playing style information is based on gameplay behavior of the player. Game parameters that defines at least some aspects of a game level forming part of the game is accessed. In an automated operation one or more of the game parameters are modified, thereby a difficulty of the game level for the player based on the one or more playing style attributes is modified. The modified game parameters are caused to be presented on a client device associated with the player of the game level.

Abstract: Techniques for efficiently detecting similarity among electronic content items are provided. A vector is generated for each of multiple content items and is used to assign its corresponding content item to a group among multiple groups. A set of content items that is assigned to a particular group of the plurality of groups is identified. For each pair of content items in the set, a similarity score is generated and used to determine whether to classify the pair as similar to each other. Similarity data is updated if to identify the pair if it is determined to classify the pair of content items as similar to each other. The similarity data associates a first content item with a second content item. The second content item is prevented from being presented to a particular user for a period of time after the first content item is presented to the particular user.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: Techniques for integrating two server systems are provided. A first server system receives, from a first computing device, provider data generated by a third-party tracking service. The first server system stores association data that associates the provider data with one or more content items. After storing the association data, the first server system receives, from a second computing device, a message that includes a first identifier and that indicates an event and a content item. In response to receiving the message, the first server system determines whether to report the event to the third-party tracking service. If so, the first server system determines a second identifier based on the first identifier, retrieves, based on the association data and the content item, the provider data that is associated with the content item, and transmits, over a network to the third-party tracking service, the second identifier and the provider data.

Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to modify a game based on playing style is provided. Player-specific playing style information that indicates one or more attributes of a playing style of a player is accessed. The playing style information is based on gameplay behavior of the player. Game parameters that defines at least some aspects of a game level forming part of the game is accessed. In an automated operation one or more of the game parameters are modified, thereby a difficulty of the game level for the player based on the one or more playing style attributes is modified. The modified game parameters are caused to be presented on a client device associated with the player of the game level.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.

Abstract: Techniques for efficiently detecting similarity among electronic content items are provided. A vector is generated for each of multiple content items and is used to assign its corresponding content item to a group among multiple groups. A set of content items that is assigned to a particular group of the plurality of groups is identified. For each pair of content items in the set, a similarity score is generated and used to determine whether to classify the pair as similar to each other. Similarity data is updated if to identify the pair if it is determined to classify the pair of content items as similar to each other. The similarity data associates a first content item with a second content item. The second content item is prevented from being presented to a particular user for a period of time after the first content item is presented to the particular user.

Abstract: Techniques for generating a multidimensional forecast are provided. In one technique, multiple segments are generated, each comprising a different set of attribute values. For each segment, a set of prior content requests for the segment is determined based on historical data, a forecasted number of content requests is determined based on the set of prior content requests, and the forecasted number of content requests is stored in association with a set of attribute values corresponding to the segment. A request is received to forecast performance of a content delivery campaign based on a particular set of attribute values. In response to receiving the request, multiple segments that share the particular set of attribute values are identified. The forecasted number of content requests associated with each segment of the multiple segments are aggregated to generate aggregated performance data. A portion of the aggregated performance data is caused to be displayed.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to modify a game based on playing style is provided. Player-specific playing style information that indicates one or more attributes of a playing style of a player is accessed. The playing style information is based on gameplay behavior of the player. Game parameters that defines at least some aspects of a game level forming part of the game is accessed. In an automated operation one or more of the game parameters are modified, thereby a difficulty of the game level for the player based on the one or more playing style attributes is modified. The modified game parameters are caused to be presented on a client device associated with the player of the game level.

Abstract: Techniques for integrating two server systems are provided. A first server system receives, from a first computing device, provider data generated by a third-party tracking service. The first server system stores association data that associates the provider data with one or more content items. After storing the association data, the first server system receives, from a second computing device, a message that includes a first identifier and that indicates an event and a content item. In response to receiving the message, the first server system determines whether to report the event to the third-party tracking service. If so, the first server system determines a second identifier based on the first identifier, retrieves, based on the association data and the content item, the provider data that is associated with the content item, and transmits, over a network to the third-party tracking service, the second identifier and the provider data.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Clustering Engine that determines that respective actions, performed in a first instance of a virtual environment by a first user during a first time range, correspond with a first latent state. The Clustering Engine determines that respective actions, performed in a second instance of the virtual environment by a second user during the first time range, correspond with a second latent state. The Clustering Engine triggers a first virtual environment feature based on a first latent state parameter space for the first user. The Clustering Engine triggers a second virtual environment feature based on a second latent state parameter space for the second user.

Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.

Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.

Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.