Abstract: The disclosed embodiments provide a system for performing inference. During operation, the system obtains a graph containing nodes representing members of an online system, edges between pairs of nodes, and edge scores representing confidences in a type of relationship between the pairs of nodes. Next, the system performs a set of iterations that propagate a label for the type of relationship from a first subset of edges to remaining edges in the graph, with each iteration updating a probability of the label for an edge between a pair of nodes based on a subset of edge scores for a second subset of edges connected to one or both nodes in the pair and probabilities of the label for the second subset of edges. The system then performs one or more tasks in the online system based on the probability of the label for the edge.

Abstract: The disclosed embodiments provide a system for performing inference. During operation, the system obtains a graph containing nodes representing members of an online system, edges between pairs of nodes, and edge scores representing confidences in a type of relationship between the pairs of nodes. Next, the system performs a set of iterations that propagate a label for the type of relationship from a first subset of edges to remaining edges in the graph, with each iteration updating a probability of the label for an edge between a pair of nodes based on a subset of edge scores for a second subset of edges connected to one or both nodes in the pair and probabilities of the label for the second subset of edges. The system then performs one or more tasks in the online system based on the probability of the label for the edge.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: A method for automatically extracting and organizing information by a processing device from a plurality of data sources is provided. A natural language processing information extraction pipeline that includes an automatic detection of entities is applied to the data sources. Information about detected entities is identified by analyzing products of the natural language processing pipeline. Identified information is grouped into equivalence classes containing equivalent information. At least one displayable representation of the equivalence classes is created. An order in which the at least one displayable representation is displayed is computed. A combined representation of the equivalence classes that respects the order in which the displayable representation is displayed is produced.

Abstract: Computer-implemented techniques for lossless and lossy summarization of large-scale graphs. Beneficially, the lossless summarization process is designed such that it can be performed in a parallel processing manner. In addition, the lossless summarization process is designed such that it can be performed with having to store only a certain small number of adjacency list node objects in-memory at once and without having to store an adjacency list representation of the entire input graph in-memory at once. In some embodiments, the techniques involve further summarizing the reduced graph output from the lossless summarization process in a lossy manner. Beneficially, the lossy summarization process uses a condition that is computationally efficient to evaluate when determining whether to drop edges of the reduced graph while at the same time ensuring the accuracy of a graph restored from the lossy reduced graph compared to the input graph is within the error bound.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a graph containing nodes, edges between the nodes, and attributes of the nodes and the edges. Next, the system stores an in-memory representation of the graph in a set of columns. The system then receives a request for performing one or more computations for traversing the graph, wherein the computation(s) include iterating through subsets of the nodes and additional subsets of the edges. To process the request, the system executes the computation(s) on the stored representation of the graph to generate a near-real-time ranking of candidates for recommending to a member of an online network. Finally, the system transmits, in a response to the request, at least a portion of the near-real-time ranking as connection recommendations in the online network.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system retrieves, from a nearline data store, one or more updates representing recent activity for a member of an online network. Next, the system performs one or more queries using data in the updates to identify a set of candidates for recommending to the member. The system then applies one or more machine learning models to features for the set of candidates to generate a ranking of the set of candidates and updates the ranking based on additional features for an additional set of candidates from an offline data store. Finally, the system outputs, to the member, at least a portion of the updated ranking as connection recommendations in the online network.

Abstract: Systems, devices, media, and methods are presented for identifying and facilitating connections in a social network. The systems and methods identify a plurality of unconnected members having a number of associations below a first threshold and identify a specified member with a number of associations above a second threshold. The systems and methods determine one or more connected members having an attenuated association with the specified member and identify a set of presentation positions within a graphical user interface. The systems and methods determine a presentation trigger selecting a connection type of a set of members to be presented to the specified member. Based on the presentation trigger, the systems and methods select an unconnected member for presentation within the set of members and cause presentation of the unconnected member within a presentation position.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Candidate Engine. The Candidate Engine generates a key(s) based on respective shared attributes between first profile data of a first member account and second profile data of a second member account in a social network service, The Candidate Engine assembles, according to encoded rules of a prediction model, feature vector data for each key. The encoded rules comprises at least one pre-defined feature predictive of an affinity between the first member account and the second member account. The Candidate Engine processes, according to the prediction model, the feature vector data for each key. The Candidate Engine receives predictive output from the prediction model.

Abstract: A method for automatically extracting and organizing information by a processing device from a plurality of data sources is provided. A natural language processing information extraction pipeline that includes an automatic detection of entities is applied to the data sources. Information about detected entities is identified by analyzing products of the natural language processing pipeline. Identified information is grouped into equivalence classes containing equivalent information. At least one displayable representation of the equivalence classes is created. An order in which the at least one displayable representation is displayed is computed. A combined representation of the equivalence classes that respects the order in which the displayable representation is displayed is produced.

Abstract: Creating training data for a natural language processing system may comprise obtaining natural language input, the natural language input annotated with one or more important phrases; and generating training instances comprising a syntactic parse tree of nodes representing elements of the natural language input augmented with the annotated important phrases. In another aspect, a classifier may be trained based on the generated training instances. The classifier may be used to predict one or more potential important phrases in a query.

Abstract: Creating training data for a natural language processing system may comprise obtaining natural language input, the natural language input annotated with one or more important phrases; and generating training instances comprising a syntactic parse tree of nodes representing elements of the natural language input augmented with the annotated important phrases. In another aspect, a classifier may be trained based on the generated training instances. The classifier may be used to predict one or more potential important phrases in a query.

Abstract: A method for automatically extracting and organizing information by a processing device from a plurality of data sources is provided. A natural language processing information extraction pipeline that includes an automatic detection of entities is applied to the data sources. Information about detected entities is identified by analyzing products of the natural language processing pipeline. Identified information is grouped into equivalence classes containing equivalent information. At least one displayable representation of the equivalence classes is created. An order in which the at least one displayable representation is displayed is computed. A combined representation of the equivalence classes that respects the order in which the displayable representation is displayed is produced.

Abstract: Techniques for improving advertisement relevance for sponsored search advertising. The method includes steps for processing a click history data structure containing at least a plurality of query-advertisement pairs, populating a first translation table containing a co-occurrence count field, populating a second translation table containing an expected clicks field, and calculating a click propensity score for an advertisement using the click history data structure, the first translation table (for determining overall click likelihood across all historical traffic), and using the second translation table (for removing biases present in the first translation table).

Abstract: An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

Abstract: An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

Abstract: The subject matter disclosed herein relates to predicting selection rates of web-based documents in response to a search query.

Abstract: A method for facilitating development of a document classification function comprises selecting a feature of a document, the feature being less than an entirety of the document; presenting the feature to a human subject; asking the human subject for a feature relevance value of the feature; and generating a classification function using the feature relevance value. The method may also include the steps of presenting the document to the human subject at the same time as presenting the feature; asking the human subject for document relevance value that measures relevance of the document to a category; and wherein the generating the classification function also uses the document relevance value.