Abstract: Disclosure includes a system, method and architecture to monitor media content streams, e.g., streams of digital encoded media data such as without limitation audio and/or video data, from a number of media content streaming providers and identify media content items from those media content items being streamed by the media content streaming providers for inclusion in a personalized media content stream for a user. In a direct streaming approach, the media content items contained in a stream are available while each item is being streamed by the content provider. In a centralized streaming approach, media content items contained in each media content stream that is currently being streamed by content provider are available for a limited window of time, which can be dependent on buffer length.

Abstract: In accordance with one aspect, methods and apparatus facilitate the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers.

Abstract: Media item recommendations, such as music track recommendations, may be made using one or more models generated using data collected from a plurality of media stream sources, such as, for example, Internet radio stations. In an initial, bootstrapping phase, data about media items and media stream playlists of media stream sources may be used to generate a model, which comprises latent factor vectors, or learned profiles, of media items, e.g., tracks, artists, etc. Such a bootstrapping phase may be performed without user data, such as user playlists and/or user feedback, to generate a model that may be used to make media item recommendations. As user data becomes available, e.g., as users of a recommendation service provide user data, the user data may be used to supplement and/or update the model and/or to create user profiles.

Abstract: Media item recommendations, such as music track recommendations, may be made using one or more models generated using data collected from a plurality of media stream sources, such as, for example, Internet radio stations. In an initial, bootstrapping phase, data about media items and media stream playlists of media stream sources may be used to generate a model, which comprises latent factor vectors, or learned profiles, of media items, e.g., tracks, artists, etc. Such a bootstrapping phase may be performed without user data, such as user playlists and/or user feedback, to generate a model that may be used to make media item recommendations. As user data becomes available, e.g., as users of a recommendation service provide user data, the user data may be used to supplement and/or update the model and/or to create user profiles.

Abstract: Media item recommendations, such as music track recommendations, may be made using one or more models generated using data collected from a plurality of media stream sources, such as, for example, Internet radio stations. In an initial, bootstrapping phase, data about media items and media stream playlists of media stream sources may be used to generate a model, which comprises latent factor vectors, or learned profiles, of media items, e.g., tracks, artists, etc. Such a bootstrapping phase may be performed without user data, such as user playlists and/or user feedback, to generate a model that may be used to make media item recommendations. As user data becomes available, e.g., as users of a recommendation service provide user data, the user data may be used to supplement and/or update the model and/or to create user profiles.

Abstract: A method and apparatus for measuring and extracting proximity in networks are disclosed. In one embodiment, the present method receives a network from a user for analysis and extraction of a smaller proximity sub-graph. The method computes a candidate sub-graph and determines at least one Cycle Free Escape Conductivity (CFEC) proximity of at least two nodes in accordance with the candidate sub-graph. The method then extracts and presents a proximity sub-graph that best captures the proximity.

Abstract: Disclosure includes a system, method and architecture to monitor media content streams, e.g., streams of digital encoded media data such as without limitation audio and/or video data, from a number of media content streaming providers and identify media content items from those media content items being streamed by the media content streaming providers for inclusion in a personalized media content stream for a user. In a direct streaming approach, the media content items contained in a stream are available while each item is being streamed by the content provider. In a centralized streaming approach, media content items contained in each media content stream that is currently being streamed by content provider are available for a limited window of time, which can be dependent on buffer length.

Abstract: Bootstrapping a recommender system that makes item recommendations. The bootstrapping identifying questions for use in interviewing a user, e.g., a new user to the recommender system, to obtain user information, e.g., user profile information, for use in predicting item ratings for the user, the predicted item ratings for use in identifying item recommendations for the user. The bootstrapping using a cost function to minimize error in selection of the questions for the interview. The bootstrapping comprising a static bootstrapping and an adaptive bootstrapping.

Abstract: Embodiments are directed towards identifying auto-folder tags for messages by using a combinational optimization approach of bi-clustering folder names and features of messages based on relationship strengths. The combinational optimization approach of bi-clustering, generally, groups a plurality of folder names and a plurality of features into one or more metafolders to optimize a cost. The cost is based on an aggregate of cut relationship strengths, where a cut results when a relationship folder name and feature are grouped in separate metafolders. Furthermore, the plurality of folder names and the plurality of features are obtained by monitoring actions of a plurality of users, where the folder names are user generated folder names and features are from a plurality of messages. The metafolders may be used to tag new user messages with an auto-folder tag.

Abstract: Systems and techniques for generating item ratings for a user in order to allow for recommendations of selected items for that user. A set of known ratings of different items for a plurality of users is collected and maintained, and these known ratings are used to estimate rating factors influencing ratings, including user and item factors. Initial user and item factors are estimated and new user and item factors are successively added, with the original rating factors being progressively shrunk so as to reduce their magnitude and their contribution to the rating estimation as successive factors are added. When an appropriate number of user and item factors has been estimated, the rating factors are used to estimate ratings of items for a user, and the estimated ratings are employed to generate recommendations for that user.

Abstract: Systems and techniques for generating item ratings for a user in order to allow for recommendations of selected items for that user. A set of known ratings of different items for a plurality of users is collected and maintained, and these known ratings are used to estimate rating factors influencing ratings, including user and item factors. Initial user and item factors are estimated and new user and item factors are successively added, with the original rating factors being progressively shrunk so as to reduce their magnitude and their contribution to the rating estimation as successive factors are added. When an appropriate number of user and item factors has been estimated, the rating factors are used to estimate ratings of items for a user, and the estimated ratings are employed to generate recommendations for that user.

Abstract: Systems and techniques for generating item ratings for a user in order to allow for recommendations of selected items for that user. A set of known ratings of different items for a plurality of users is collected and maintained, and these known ratings are used to estimate rating factors influencing ratings, including user and item factors. Initial user and item factors are estimated and new user and item factors are successively added, with the original rating factors being progressively shrunk so as to reduce their magnitude and their contribution to the rating estimation as successive factors are added. When an appropriate number of user and item factors has been estimated, the rating factors are used to estimate ratings of items for a user, and the estimated ratings are employed to generate recommendations for that user.

Abstract: Embodiments are directed towards identifying auto-folder tags for messages by using a combinational optimization approach of bi-clustering folder names and features of messages based on relationship strengths. The combinational optimization approach of bi-clustering, generally, groups a plurality of folder names and a plurality of features into one or more metafolders to optimize a cost. The cost is based on an aggregate of cut relationship strengths, where a cut results when a relationship folder name and feature are grouped in separate metafolders. Furthermore, the plurality of folder names and the plurality of features are obtained by monitoring actions of a plurality of users, where the folder names are user generated folder names and features are from a plurality of messages. The metafolders may be used to tag new user messages with an auto-folder tag.

Abstract: Bootstrapping a recommender system that makes item recommendations. The bootstrapping identifying questions for use in interviewing a user, e.g., a new user to the recommender system, to obtain user information, e.g., user profile information, for use in predicting item ratings for the user, the predicted item ratings for use in identifying item recommendations for the user. The bootstrapping using a cost function to minimize error in selection of the questions for the interview. The bootstrapping comprising a static bootstrapping and an adaptive bootstrapping.

Abstract: Example collaborative filtering techniques provide improved recommendation prediction accuracy by capitalizing on the advantages of both neighborhood and latent factor approaches. One example collaborative filtering technique is based on an optimization framework that allows smooth integration of a neighborhood model with latent factor models, and which provides for the inclusion of implicit user feedback. A disclosed example Singular Value Decomposition (SVD)-based latent factor model facilitates the explanation or disclosure of the reasoning behind recommendations. Another example collaborative filtering model integrates neighborhood modeling and SVD-based latent factor modeling into a single modeling framework. These collaborative filtering techniques can be advantageously deployed in, for example, a multimedia content distribution system of a networked service provider.

Abstract: Systems and techniques for estimation of item ratings for a user. A set of item ratings by multiple users is maintained, and similarity measures for all items are precomputed, as well as values used to generate interpolation weights for ratings neighboring a rating of interest to be estimated. A predetermined number of neighbors are selected for an item whose rating is to be estimated, the neighbors being those with the highest similarity measures. Global effects are removed, and interpolation weights for the neighbors are computed simultaneously. The interpolation weights are used to estimate a rating for the item based on the neighboring ratings, Suitably, ratings are estimated for all items in a predetermined dataset that have not yet been rated by the user, and recommendations are made of the user by selecting a predetermined number of items in the dataset having the highest estimated ratings.

Abstract: Disclosed are methods and apparatus for facilitating the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers.

Abstract: Systems and methods to provide a recommendation engine that uses implicit feedback observations are provided. A particular method includes receiving accessing data comprising a plurality of implicit feedback observations for a plurality of users. The plurality of users includes a first user that requested a recommendation. Each implicit feedback observation is associated with a particular user and a particular item of a plurality of items. The method includes determining a plurality of preference ratings and a plurality of confidence ratings for each user of the plurality of users for each item based on the plurality of implicit feedback observations. The method includes generating a recommendation list of one or more of the plurality of items for the first user based on the plurality of preference ratings and the plurality of confidence ratings.

Abstract: Certain exemplary embodiments comprise a method, comprising: in a sensor node network comprising a plurality of sensor nodes, each sensor node from the plurality of sensor nodes having location coordinates that are initially unknown, each sensor node from the plurality of sensor nodes having neighbors, each sensor node from the plurality of sensor nodes capable of determining distances only to that sensor node's neighbors: for each sensor node from the plurality of sensor nodes, communicating with that sensor node's neighbors; and based only on communications between neighbors, in a fully distributed manner, generating a sensor node network layout that indicates the location coordinates of each sensor node.

Abstract: A method and system for graph mining direction-aware proximity measurements. A directed graph includes nodes and directed edges connecting the nodes. A direction-aware proximity measurement is calculated from a first node to a second node or from a first group of nodes to a second group of nodes. The direction-aware proximity measurement from a first node to second node is based on an escape probability from the first node to the second node. Disclosed herein are methods for efficiently calculating one or multiple direction-aware proximity measurements. The direction-aware proximity measurements can be used in performing various graph mining applications.