Abstract: Methods, systems, and computer program products for generating a setting recommendation to configure a revenue management system of a travel provider. The setting recommendation may be generated by collecting data from a articles provided by data sources associated with a given market. Each article may be compared to a filter in a set of filters to determine whether the article meets a criterion of the filter. If an article meets the criterion of a filter, the article may be parsed and data may be selectively collected for use in forecasting a future event regarding the given market. A recommended action may be generated based on the future event. Information regarding the recommended action, the filter, the article, and the future event may be provided in a setting recommendation for use in updating a system setting in an effort to address the effect of the future event.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A set of language tags in a backend application can be passed to a frontend application in the form of a language selection tool. The disclosure creates a mapping between the frontend and backend application of at least one non-default language descriptor string onto a language tag of the backend application. The frontend language selection tool then displays to the user the non-default language descriptor string. The use of non-default language descriptor strings clarifies to the user otherwise ambiguous language identifiers.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A set of language tags in a backend application can be passed to a frontend application in the form of a language selection tool. The disclosure creates a mapping between the frontend and backend application of at least one non-default language descriptor string onto a language tag of the backend application. The frontend language selection tool then displays to the user the non-default language descriptor string. The use of non-default language descriptor strings clarifies to the user otherwise ambiguous language identifiers.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A process for a language selection using one or more language preferences is disclosed. Information for presenting a language preference selection control may be sent to a user. A language preference may be received from the user and stored to a server. A cookie, which includes an indication of the language preference, may be caused to be stored to a device of a user. The stored language preference may be accessed from the cookie responsive to a communication from the user. Responsive to whether the user has logged into an account, the language preference may be retrieved from either the server or from the cookie. The retrieved language preference may be compared with languages available in an application and a most preferred language for the application may be determined based upon the comparison. Information from the application may be provided to the user in the most preferred language.

Abstract: An ordered list of language indicators may be obtained, wherein each of the language indicators is either user defined or system defined. Each language indicator may be ranked according to a priority of the language indicator for determining a most-preferred language for a user. A list of languages may be received that indicates the languages available in an application. The ordered list of language indicators may then be compared to the received languages, and a highest-ranked language indicator determined. The most-preferred language for the user may be selected based upon the highest-ranked language indicator. Information from the application may then be provided to the user in the most preferred language.