Abstract: Aspects of the technology described herein use acoustic features of a music track to capture information for a recommendation system. The recommendation can work without analyzing label data (e.g., genre, artist) or usage data for a track. For each audio track, a descriptor is generated that can be used to compare the track to other tracks. The comparisons between track descriptors result in a similarity measure that can be used to make a recommendation. In this process, the audio descriptors are used directly to form a track-to-track similarity measure between tracks. By measuring the similarity between a track that a user is known to like and an unknown track, a decision can be made whether to recommend the unknown track to the user.

Abstract: Described is a technology by which a seamless automatic connection to an (e.g., corporate) network is made for a client device. Upon detecting a need for a connection to a network, such as by intercepting a communication directed towards a network destination, a list of available connection methods is automatically obtained based on the device's current location data (e.g., LAN or remote) and policy information. An available connection method from the list is selected, e.g., in order, and an attempt is made to establish a connection via that connection method. If the attempt fails, another attempt is made with a different connection method, and so on, until a connection method succeeds. Additional seamlessness from the user's perspective is provided via a credentials vault, by which stored credentials may be retrieved and used in association with the access method being attempted.

Abstract: Systems and methods are provided for optimizing data storage and improving the speed of data retrieval in a data store, such as a storage server connected to a large network through the use of bucketing techniques to create efficient data structures for storing received key-value datasets at one or more storage servers. Fast key-value read requests and key-value retrievals may be accomplished through the use of multiphase lookup operations on the one or more storage servers. The system is optimized for best performance of retrieval through the separation of the write and read mechanisms. Systems and methods provided herein control the level of wastefulness on the back end of a system and reduce read operation inefficiencies on the front end of a system.

Abstract: A recommender system that represents items in a catalog by first feature vectors in a first vector space based on first characteristics of the items and second feature vectors in a second vector space based on second characteristics of the items different from the first characteristics and maps a feature vector defined in the first vector space for an item to a vector in the second vector space to provide recommendations based on the item.

Abstract: Aspects of the subject matter described herein relate to a simplified login for mobile devices. In aspects, on a first logon, a mobile device asks a user to enter credentials and a PIN. The credentials and PIN are sent to a server which validates user credentials. If the user credentials are valid, the server encrypts data that includes at least the user credentials and the PIN and sends the encrypted data to the mobile device. In subsequent logons, the user may logon using only the PIN. During login, the mobile device sends the PIN in conjunction with the encrypted data. The server can then decrypt the data and compare the received PIN with the decrypted PIN. If the PINs are equal, the server may grant access to a resource according to the credentials.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: A playlist generator that utilizes multiple data sources to rank each track within a set of candidate tracks to enable selection of candidate tracks according to the ranking. Candidate tracks are each scored according to one or more features, such as acoustic similarity and/or similar usage patterns of the candidate track or artist of the candidate track to a current or previously played track or artist. Each feature is weighted according to historical listening patterns surrounding a user-selected playlist seed artist. The weighting may also be further corrected according to historical listening patterns of the particular user. When historical usage data related to a particular seed artist is limited, more generalized historical usage data related to a higher level in a genre hierarchy may be used.

Abstract: Aspects of the subject matter described herein relate to a simplified login for mobile devices. In aspects, on a first logon, a mobile device asks a user to enter credentials and a PIN. The credentials and PIN are sent to a server which validates user credentials. If the user credentials are valid, the server encrypts data that includes at least the user credentials and the PIN and sends the encrypted data to the mobile device. In subsequent logons, the user may logon using only the PIN. During login, the mobile device sends the PIN in conjunction with the encrypted data. The server can then decrypt the data and compare the received PIN with the decrypted PIN. If the PINs are equal, the server may grant access to a resource according to the credentials.

Abstract: A computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

Abstract: A method for producing item recommendations for user consumption from usage data of items as they are consumed in combinations or baskets; breaking the baskets into positive pairs of items appearing in the baskets; finding negative pairs of items appearing relatively frequently in the baskets but not in the positive pairs; embedding all the items of the global catalog or universal set into a latent space such that items appearing together more often in the positive pairs are relatively close together and items appearing together in the negative pairs are relatively far apart; obtaining a selection from a user of a first item for consumption and providing to the user at least one suggestion of a second item for further consumption, the second item not being identical with the first item, the second item being an item located most closely in the latent space to the first item for consumption.

Abstract: Embodiments of the disclosure relate to apparatus for recommending items from a catalog of items to users in a population of users, configured to determine values for a measure of association between transactions of users with items in a first catalog and transactions of users with items in a second catalog and provide recommendations to users for transacting with items in the catalogs based on the determined values of association.

Abstract: A system and method for basket completion for items contained in a catalog, uses the determinantal point process on a closed set of items in a catalog. A parameter space contains the items of the catalog as vectors of parameters whose values are obtained using the determinantal point process in a learning process. Subsequently a user input obtains a selection from a user of one or more items from the catalogue. Then a selector selects another item within the parameter space whose vector forms a largest area when combined with the vectors of the already present items. The large area implies both popularity of the item and complementarity of the new item with the items already chosen. The user is provided with the new item to complete a basket with the already present items.

Abstract: A computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

Abstract: Example apparatus and methods transform a non-metric latent space produced by a matrix factorization process to a higher dimension metric space by applying an order preserving transformation to the latent space. The transformation preserves the order of the results of an inner product operation defined for the latent space. The higher dimension metric space may be queried for the results to different requests. Example apparatus and methods may assign every user i a vector ui in a latent space, and may assign every item j a vector vj in the latent space. The dot product ui·vj represents the score between the user i and the item j. The score represents the strength of the relationship between the user i and the item j. Example apparatus and methods may then apply ranking methodologies (e.g., LSH, K-D trees) to problems including recommendation, targeting, matchmaking, or item to item.

Abstract: A network with authentication implemented using a client health enforcement framework. The framework is adapted to receive plug-ins on clients that generate health information. Corresponding plug-ins on a server validate that health information. Based on the results of validation, the server may instruct the client to remediate or may authorize an underlying access enforcement mechanism to allow access. A client plug-in that generates authentication information formatted as a statement of health may be incorporated into such a framework. Similarly, on the server, a validator to determine, based on the authentication information, whether the client should be granted network access can be incorporated into the framework. Authentication can be simply applied or modified by changing the plug-ins, while relying on the framework to interface with an enforcement mechanism.

Abstract: A computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

Abstract: Example apparatus and methods transform a non-metric latent space produced by a matrix factorization process to a higher dimension metric space by applying an order preserving transformation to the latent space. The transformation preserves the order of the results of an inner product operation defined for the latent space. The higher dimension metric space may be queried for the results to different requests. Example apparatus and methods may assign every user i a vector ui in a latent space, and may assign every item j a vector vj in the latent space. The dot product ui·vj represents the score between the user i and the item j. The score represents the strength of the relationship between the user i and the item j. Example apparatus and methods may then apply ranking methodologies (e.g., LSH, K-D trees) to problems including recommendation, targeting, matchmaking, or item to item.

Abstract: Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between users and items and between items and items in the usage matrix. The usage matrix relates users to items according to a collaborative filtering approach. A cell in the usage matrix may store a value that describes whether a user has acquired an item and the strength with which the user likes an item that has been acquired. The latent item space may reflect true relationships between items represented in the usage matrix and those relationships may be proportional to the strength in the usage matrix. The strength of the relationship may be encoded using continuous data that measures, for example, the amount of time a video game has been played, the amount of time content has been viewed, or other continuous or cumulative engagement measurements.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: Example apparatus and methods produce an explanation of why a recommendation is being made by an automated collaborative filtering recommendation system. The explanation may include feature categories and features that describe the item being recommended. The feature categories selected and features selected may depend on a personalization level for an item associated with the recommendation, a quality level of the descriptiveness of a feature for the recommendation, and correlations between items and features analyzed by the recommendation system. The feature categories and features may be selected based on an aggregate score that considers and combines the personalization level, the quality level, and the correlations. The quality level may be human curated or may vary directly with the ability of a feature to partition a feature space. Correlations between items and features reflect the degree to which the features are exhibited by the items.