Patent application number | Description | Published |
20120030020 | COLLABORATIVE FILTERING ON SPARE DATASETS WITH MATRIX FACTORIZATIONS - A system, method and computer program product automatically present at least one product to at least one client for at least one possible purchase. The system applies a matrix factorization on a binary matrix X representing which clients purchased which products. The system optimizes zero-valued elements in the matrix X that correspond to unknown client-product affinities. The system constructs based on the optimization, a prediction matrix {circumflex over (X)} whose each element value represents a likelihood that a corresponding client purchases a corresponding product. The system identifies at least one client-product pair with the highest value in the matrix {circumflex over (X)}. The system recommends at least one product to at least one client according to the client-product pair with the highest value. | 02-02-2012 |
20120046992 | ENTERPRISE-TO-MARKET NETWORK ANALYSIS FOR SALES ENABLEMENT AND RELATIONSHIP BUILDING - There are provided a system, a method and a computer program product for increasing of productivity of sales force in a first entity. The system locates or constructs at least one enterprise social network in the first entity. The system constructs at least one market social network. The system creates at least one connection between the enterprise social network and the market social network. Sales representative in the first entity expands new sales operations and/or identify new markets via the connected social networks. | 02-23-2012 |
20120143796 | GROUP VARIABLE SELECTION IN SPATIOTEMPORAL MODELING - In response to issues of high dimensionality and sparsity in machine learning, it is proposed to use a multiple output regression modeling module that takes into account information on groups of related predictor features and groups of related regressions, both given as input, and outputs a regression model with selected feature groups. Optionally, the method can be employed as a component in methods of causal influence detection, which are applied on a time series training data set representing the time-evolving content generated by community members, output a model of causal relationships and a ranking of the members according to their influence. | 06-07-2012 |
20120143815 | INFERRING INFLUENCE AND AUTHORITY - A computer model finds an originating community member or members, for instance amongst bloggers, scientific researchers, or phenomena in phenomenological systems. In one embodiment, non-explicit causal relationships may be inferred from comparing blogs as a whole. Influence relationships are derived from a weighted, directed graph output and sources of influence are ranked. This implementation is useful for a variety of applications. | 06-07-2012 |
20130018827 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIESAANM He; JingruiAACI OssiningAAST NYAACO USAAGP He; Jingrui Ossining NY USAANM Lawrence; Richard D.AACI RidgefieldAAST CTAACO USAAGP Lawrence; Richard D. Ridgefield CT USAANM Melville; PremAACI White PlainsAAST NYAACO USAAGP Melville; Prem White Plains NY USAANM Sindhwani; VikasAACI HawthorneAAST NYAACO USAAGP Sindhwani; Vikas Hawthorne NY USAANM Chenthamarakshan; Vijil E.AACI OssiningAAST NYAACO USAAGP Chenthamarakshan; Vijil E. Ossining NY US - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130018828 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIES - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130151520 | INFERRING EMERGING AND EVOLVING TOPICS IN STREAMING TEXT - A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify a first group of topics as evolving topics and a second group of topics as emerging topics. The matrices includes a first matrix X identifying a multitude of words in each of the documents, a second matrix W identifying a multitude of topics in each of the documents, and a third matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, the documents form a streaming dataset, and two forms of temporal regularizers are used to help identify the evolving topics and the emerging topics in the streaming dataset. | 06-13-2013 |
20130151525 | INFERRING EMERGING AND EVOLVING TOPICS IN STREAMING TEXT - A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify evolving topics and emerging topics. The matrices includes a matrix X identifying a multitude of words in each of the documents, a matrix W identifying a multitude of topics in each of the documents, and a matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, two forms of temporal regularizers are used to help identify the evolving and emerging topics. In another embodiment, a two stage approach involving detection and clustering is used to help identify the evolving and emerging topics. | 06-13-2013 |
20140019388 | SYSTEM AND METHOD FOR LOW-RANK MATRIX FACTORIZATION FOR DEEP BELIEF NETWORK TRAINING WITH HIGH-DIMENSIONAL OUTPUT TARGETS - Systems and methods for reducing a number of training parameters in a deep belief network (DBN) are provided. A method for reducing a number of training parameters in a deep belief network (DBN) comprises determining a network architecture including a plurality of layers, using matrix factorization to represent a weight matrix of a final layer of the plurality of layers as a plurality of matrices, and training the DBN having the plurality of matrices. | 01-16-2014 |