Patent application number | Description | Published |
20110218946 | PRESENTING CONTENT ITEMS USING TOPICAL RELEVANCE AND TRENDING POPULARITY - A user may request a presentation of a content item set, such as a social network comprising a set of status messages or an image database comprising a set of images. However, the volume and diversity of content items of the content item set may reduce the interest of the user in the presented content items. The potential interest of the user in the presented content items may be improved by selecting content items that are associated with one or more topics of potential interest to the user, and having a positive trending popularity among users of the content item set. Moreover, the interaction of the user with a presented content item may be monitored and used to determine the interest of the user in the topics associated with the presented content item and the popularity of the content item. | 09-08-2011 |
20110320767 | Parallelization of Online Learning Algorithms - Methods, systems, and media are provided for a dynamic batch strategy utilized in parallelization of online learning algorithms. The dynamic batch strategy provides a merge function on the basis of a threshold level difference between the original model state and an updated model state, rather than according to a constant or pre-determined batch size. The merging includes reading a batch of incoming streaming data, retrieving any missing model beliefs from partner processors, and training on the batch of incoming streaming data. The steps of reading, retrieving, and training are repeated until the measured difference in states exceeds a set threshold level. The measured differences which exceed the threshold level are merged for each of the plurality of processors according to attributes. The merged differences which exceed the threshold level are combined with the original partial model states to obtain an updated global model state. | 12-29-2011 |
20120158791 | FEATURE VECTOR CONSTRUCTION - Feature vector construction techniques are described. In one or more implementations, an input is received at a computing device that describes a graph query that specifies one of a plurality of entities to be used to query a knowledge base graph that represents the plurality of entities. A feature vector is constructed, by the computing device, having a number of indicator variables, each of which indicates observance of a sub-graph feature represented by a respective indicator variable in the knowledge base graph. | 06-21-2012 |
20140188928 | RELATIONAL DATABASE MANAGEMENT - New methods of relational database management are described, for example, to enable completion and checking of data in relational databases, including completion of missing foreign key values, to facilitate understanding of data in relational databases, to highlight data that it would be useful to add to a relational database and for other applications. In various embodiments, the schema of a relational database is used to automatically create a probabilistic graphical model that has a structure related to the schema. For example, nodes representing individual rows are linked to rows of other tables according to the database schema. In examples, data in the relational database is used to carry out inference using inference algorithms derived from the probabilistic graphical model. In various examples, inference results, comprising probability distributions each for an individual table cell, are used to fill missing data, highlight errors, and for other purposes. | 07-03-2014 |
20150058337 | DATABASE ACCESS - Database access is described, for example, where data in a database is accessed by an inference engine. In various examples, the inference engine executes inference algorithms to access data from the database and carry out inference using the data. In examples the inference algorithms are compiled from a schema of the database which is annotated with expressions of probability distributions over data in the database. In various examples the schema of the database is modified by adding one or more latent columns or latent tables to the schema for storing data to be inferred by the inference engine. In examples the expressions are compositional so, for example, an expression annotating a column of a database table may be used as part of an expression annotating another column of the database. | 02-26-2015 |