Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.

Abstract: A social newsfeed being delivered to a user is triaged. A personalized model is established which predicts the importance to the user of data elements within a current social newsfeed being delivered to the user. The personalized model is established based on implicit actions the user takes in response to receiving previous social newsfeeds. The personalized model is then used to triage the data elements within the current social newsfeed.

Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.

Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.

Abstract: A method for generating a Bayesian network in a parallel manner is based on an initial model having a plurality of nodes. Each node corresponds to a variable of a data set and has a local distribution associated therewith. The method includes assigning a plurality of subsets of the nodes to a respective plurality of constructors. The plurality of constructors is operated in a parallel manner to identify edges to add between nodes in the initial model. The identified edges are added to the initial model to generate the Bayesian network. The edges indicate dependency between nodes connected by the edges.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate evaluation of digital content preferences are provided. A user is presented with items of digital content and permitted to manipulate the arrangement of the digital content items in the context of a layout area. Based on the user's manipulation of the digital content items, a user preference regarding an arrangement of digital content, such as a location preference, a position preference, and/or a usage preference, is identified. In embodiments, such a user preference can be utilized to later display digital content to a user in accordance therewith.

Abstract: A “Music Mapper” automatically constructs a set coordinate vectors for use in inferring similarity between various pieces of music. In particular, given a music similarity graph expressed as links between various artists, albums, songs, etc., the Music Mapper applies a recursive embedding process to embed each of the graphs music entries into a multi-dimensional space. This recursive embedding process also embeds new music items added to the music similarity graph without reembedding existing entries so long a convergent embedding solution is achieved. Given this embedding, coordinate vectors are then computed for each of the embedded musical items. The similarity between any two musical items is then determined as either a function of the distance between the two corresponding vectors. In various embodiments, this similarity is then used in constructing music playlists given one or more random or user selected seed songs or in a statistical music clustering process.

Abstract: A method and system for generating a price landscape for an advertiser for bids placed by the advertiser for advertisement space is provided. A price landscape system generates a price landscape based on information provided by an advertisement placement service that may include overall price estimation data and advertiser-specific performance data. The price landscape system generates price landscape data for an advertiser that combines the overall price estimation data and the advertiser-specific performance data to provide a more accurate assessment of the advertiser's expected performance than can be determined from the overall price estimation data or the advertiser-specific performance data alone.

Abstract: A method for generating a Bayesian network in a parallel manner is based on an initial model having a plurality of nodes. Each node corresponds to a variable of a data set and has a local distribution associated therewith. The method includes assigning a plurality of subsets of the nodes to a respective plurality of constructors. The plurality of constructors is operated in a parallel manner to identify edges to add between nodes in the initial model. The identified edges are added to the initial model to generate the Bayesian network. The edges indicate dependency between nodes connected by the edges.

Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.

Abstract: A “Music Mapper” automatically constructs a set coordinate vectors for use in inferring similarity between various pieces of music. In particular, given a music similarity graph expressed as links between various artists, albums, songs, etc., the Music Mapper applies a recursive embedding process to embed each of the graphs music entries into a multi-dimensional space. This recursive embedding process also embeds new music items added to the music similarity graph without reembedding existing entries so long a convergent embedding solution is achieved. Given this embedding, coordinate vectors are then computed for each of the embedded musical items. The similarity between any two musical items is then determined as either a function of the distance between the two corresponding vectors. In various embodiments, this similarity is then used in constructing music playlists given one or more random or user selected seed songs or in a statistical music clustering process.