Abstract: Some implementations relate to methods, systems, and a computing device to generate predicted scores for games based on gameplay duration. In some implementations, a method includes obtaining game session data that includes a respective session duration for each game session of a plurality of game sessions, each game session associated with a respective game and a respective game player. The method further includes grouping the plurality of games game clusters based on the game session data, each game cluster including one or more of the games such that no game is in more than one cluster. The method further includes generating a predicted score for one or more of the games and identifying at least one game as a high quality game based on the predicted score. In some implementations, generating the predicted store includes determining a distance between each game and the decision boundary.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to personalize recommendations. In some implementations, a method includes computing a user-based similarity matrix and a game-based similarity matrix, obtaining a set of user scores from a first machine learning model and the user-based similarity matrix, and obtaining a set of game scores from a second machine learning model and the game-based similarity matrix. The method also includes combining the set of user scores and the set of game scores to generate a normalized set of scores, identifying a set of personalized recommendations for the user associated with a subset of the normalized set of scores, assigning a rank to each of the plurality of items, generating a list of items wherein items in the list of items are ordered based on respective ranks, and providing a user interface to the user that includes the list of items.

Abstract: Some implementations relate to methods, systems, and a computing device to generate predicted scores for games based on gameplay duration. In some implementations, a method includes obtaining game session data that includes a respective session duration for each game session of a plurality of game sessions, each game session associated with a respective game and a respective game player. The method further includes grouping the plurality of games game clusters based on the game session data, each game cluster including one or more of the games such that no game is in more than one cluster. The method further includes generating a predicted score for one or more of the games and identifying at least one game as a high quality game based on the predicted score. In some implementations, generating the predicted store includes determining a distance between each game and the decision boundary.

Abstract: A system and method for preventing fraud in an online transaction is shown. Requests for financial transactions between on-line account holders are detected as well as relationships between the accounts. A fraud value related to a likelihood that a fraud is occurring in the transactions is determined based on the relationship. An indication of a likelihood of fraud is provided if the fraud value exceeds a predetermined amount. Thus the transaction is terminated and the appropriate parties are automatically notified.