Abstract: A machine learning method comprises: receiving input data comprising a set of feature variables each indicating features of a plurality of entities; encoding partial order relations between entities of the plurality of entities as a set of logic rules; constructing a hypergraph comprising a plurality of vertices representing the entities of the plurality of entities and a plurality of hyperlinks linking similar entities of the plurality of entities; adding directed edges to the hypergraph to represent partial order interactions between entities to form a partial order hypergraph; representing the partial order hypergraph as a set of matrices; forming an objective function using the logic rules and matrices of the set of matrices; minimizing the objective function to obtain a prediction function; and outputting an indication of the prediction function.

Abstract: Methods and systems for detecting visual relations in a video are disclosed.

Abstract: Methods and systems for detecting visual relations in a video are disclosed.

Abstract: A machine learning method comprises: receiving input data comprising a set of feature variables each indicating features of a plurality of entities; encoding partial order relations between entities of the plurality of entities as a set of logic rules; constructing a hypergraph comprising a plurality of vertices representing the entities of the plurality of entities and a plurality of hyperlinks linking similar entities of the plurality of entities; adding directed edges to the hypergraph to represent partial order interactions between entities to form a partial order hypergraph; representing the partial order hypergraph as a set of matrices; forming an objective function using the logic rules and matrices of the set of matrices; minimizing the objective function to obtain a prediction function; and outputting an indication of the prediction function.

Abstract: Methods and systems for predictive analysis are disclosed. A predictive analysis method comprises receiving input data comprising an indication of a user, an indication of an item, a user feature vector indicating features of the user and an item feature vector indicating features of the item; and constructing a cross feature vector indicating values for cross features between features of the user and features of the user. Embedding vectors derived from the cross feature vector, the user feature vector and the item feature vector are input into an attention network to determine a set of attentive weights which indicate cross features between the user and item features. These cross features are used in the identification of a user item preference.

Abstract: A method of tracking microblog messages for relevancy to an entity identifiable by an associated text and an image is disclosed. The method comprises (i) performing a search on the microblog messages based on the associated text to obtain a first set of results; (ii) performing image detection on the first set of results based on the associated image to obtain a set of seed messages; (iii) performing a search on the microblog messages based on a set of characteristics derived from the seed messages to obtain a second set of results; and (iv) selecting entries from the first and second sets of results based on relevancy to the entity, wherein the set of characteristics are associated to the entity. A related apparatus is also disclosed.

Abstract: A system provides product annotation in a video to one or more users. The system receives a video from a user, where the video includes multiple video frames. The system extracts multiple key frames from the video and generates a visual representation of the key frame. The system compares the visual representation of the key frame with a plurality of product visual signatures, where each visual signature identifies a product. Based on the comparison of the visual representation of the key frame and a product visual signature, the system determines whether the key frame contains the product identified by the visual signature of the product. To generate the plurality of product visual signatures, the system collects multiple training images comprising multiple of expert product images obtained from an expert product repository, each of which is associated with multiple product images obtained from multiple web resources.

Abstract: Various embodiments relate to methods, apparatuses and computer-readable mediums for organizing data relating to a product. An embodiment relates to a method for generating a modified hierarchy for a product based on data relating to the product. The method includes generating an initial hierarchy for the product, the initial hierarchy comprising a plurality of nodes, each node representing a different product aspect, the plurality of nodes being interconnected in dependence on relationships between different product aspects. The method also includes identifying a product aspect from the data. The method additionally includes determining an optimal position in the initial hierarchy for the identified product aspect by computing an objective function. The method further includes inserting the identified product aspect into the optimal position in the initial hierarchy to generate the modified hierarchy.

Abstract: Frequently Asked Questions (FAQ) data are generated using Community-based Question Answering (CQA) data. A thematic hierarchy generation module receives multiple data sources and generates a thematic hierarchy of the data source, where a data source has one or more topics and a topic has one or more themes. A feature classifier classifies multiple CQA data into one or more themes based on the thematic hierarchy, where a CQA data contains multiple question-answer pairs. A selection module selects multiple question-answer pairs from the CQA data based on the classification, measures the quality of the selected question-answer pairs and generates FAQ data using the selected question-answer pairs of the CQA data.

Abstract: A system provides product annotation in a video to one or more users. The system receives a video from a user, where the video includes multiple video frames. The system extracts multiple key frames from the video and generates a visual representation of the key frame. The system compares the visual representation of the key frame with a plurality of product visual signatures, where each visual signature identifies a product. Based on the comparison of the visual representation of the key frame and a product visual signature, the system determines whether the key frame contains the product identified by the visual signature of the product. To generate the plurality of product visual signatures, the system collects multiple training images comprising multiple of expert product images obtained from an expert product repository, each of which is associated with multiple product images obtained from multiple web resources.

Abstract: Frequently Asked Questions (FAQ) data are generated using Community-based Question Answering (CQA) data. A thematic hierarchy generation module receives multiple data sources and generates a thematic hierarchy of the data source, where a data source has one or more topics and a topic has one or more themes. A feature classifier classifies multiple CQA data into one or more themes based on the thematic hierarchy, where a CQA data contains multiple question-answer pairs. A selection module selects multiple question-answer pairs from the CQA data based on the classification, measures the quality of the selected question-answer pairs and generates FAQ data using the selected question-answer pairs of the CQA data.