Abstract: The present disclosure describes one or more systems, methods, routines and/or techniques for automatic detection of fraudulent ratings and/or comments related to an application store. The present disclosure describes various ways to differentiate fraudulent submissions (e.g., ratings, comments, reviews, etc.) from legitimate submissions, e.g., submissions by real users of an application. These various ways may be used to generate intermediate signals that may indicate that a submission is fraudulent. One or more intermediate signals may be automatically combined or aggregated to generate a detection conclusion for a submission. Once a fraudulent submission is detected, the present disclosure describes various ways to proceed (e.g., either automatically or manually), for example, the fraudulent submission may be ignored, or a person or account associated with the fraudulent submission may be penalized.

Abstract: Methods and apparatus related to word sense disambiguation utilizing hypernyms. In some implementations, one or more senses of a word are determined based on hypernyms for the word and an association of the word to the one or more senses is stored. In some implementations, a target word in a textual segment is identified and a word sense to assign to the target word is determined based on hypernyms that are associated with the target word.

Abstract: Applications that have been designed for a smaller format device such as a smartphone and simply ported to a larger format device such as a tablet can be discerned from applications designed specifically for the larger format device. An application can be evaluated based on tablet compatibility criteria and can be assigned a tablet compatibility score. The application can be evaluated based on quality criteria and can be assigned a quality score. The compatibility score and the quality score can be used to help find and rate any number of applications.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a textual term; determining, by one or more computers, a vector representing a phonetic feature of the textual term; comparing the vector representing the phonetic feature of the textual term with a reference vector representing a phonetic feature of a reference textual term; and classifying the textual term based on the comparing the vector with the reference vector.

Abstract: Applications that have been designed for a smaller format device such as a smartphone and simply ported to a larger format device such as a tablet can be discerned from applications designed specifically for the larger format device. An application can be evaluated based on tablet compatibility criteria and can be assigned a tablet compatibility score. The application can be evaluated based on quality criteria and can be assigned a quality score. The compatibility score and the quality score can be used to help find and rate any number of applications.

Abstract: A server that manages download and/or distribution of content may collect content-related information associated with users, and classify the users based on that data. The content-related information may comprise data relating to content generation and/or upload by the users. The server may determine whether a user is granted permission to upload content for distribution or download via the server, based on correlating the user with a previously classified user, and/or on evaluation of current content generation or download activities associated with the user. Determination of whether the user is granted permission to upload content may be done directly and/or autonomously by the server. Alternatively, a recommendation whether to grant permission to upload content may be submitted by the server to another entity for selection thereby. The server may reject or accept a content upload request from the user based on the determination of whether the user is granted permission.

Abstract: The present disclosure describes one or more systems, methods, routines and/or techniques for automatic detection of fraudulent ratings and/or comments related to an application store. The present disclosure describes various ways to differentiate fraudulent submissions (e.g., ratings, comments, reviews, etc.) from legitimate submissions, e.g., submissions by real users of an application. These various ways may be used to generate intermediate signals that may indicate that a submission is fraudulent. One or more intermediate signals may be automatically combined or aggregated to generate a detection conclusion for a submission. Once a fraudulent submission is detected, the present disclosure describes various ways to proceed (e.g., either automatically or manually), for example, the fraudulent submission may be ignored, or a person or account associated with the fraudulent submission may be penalized.