Abstract: A computer-implemented method of locating points of interest for a user in a geographic area is disclosed. A current state of the user is used as an index into a list of a plurality of current state functions to select at least one of the plurality of current state functions. Each current state function corresponds with a utility measure set where each utility measure set includes a plurality of utility measures ordered in a selected sequence. The computer is used to perform: (i) lexicographic optimization with respect to the plurality of current state functions so that the selected sequence of each utility measure set is optimally ordered, and (ii) rate a plurality of points of interest with respect to at least one of the plurality of optimized current state functions, and, based on the rating, to automatically select a set of points of interest for the user.

Abstract: A method of training a hypergraph convolutional network (HGCN) includes: receiving training data including search instances and recommendation instances; constructing a hypergraph from the training data, where each node of the hypergraph represents one of a user profile, a query term, and a content item, and where the hypergraph represents each of the search instances and each of the recommendation instances as a hyperedge linking corresponding ones of the nodes; initializing base embeddings associated with the hypergraph nodes; propagating the base embeddings through one or more convolutional layers of the HGCN to obtain, for each of the convolutional layers, respective embeddings of the nodes of the hypergraph; computing, based on the base embeddings and the respective embeddings obtained from each of the one or more convolutional layers: a first loss; and a second loss; and selectively updating ones of the base embeddings based on the first and second losses.

Abstract: A method for ranking a set of objects includes: receiving the set of objects to rank, a relevance score for each object, and a set of objective functions; based on the relevance scores for the objects, defining a decision space having n decision variables using a polytope, where n is the number of objects to rank and where vertices of the polytope represent permutations of exposures provided to the objects in the set by corresponding rankings; determining a Pareto-set for the set of objective functions; based on a Pareto-optimal point in the Pareto-set, determining a distribution over rankings for the objects in the set using the decision space, where a proportion is associated with each ranking in the distribution; selecting a sequence of rankings for the objects in the set based on the distribution in accordance with their proportions; and outputting the selected sequence of rankings of the objects.

Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

Abstract: A computer-implemented method for ranking a set of objects includes: receiving a set of objects and a set of objective functions; defining a decision space using a permutohedron having n decision variables, where n is the number of objects to rank and where vertex coordinates of the permutohedron represent exposure associated with the corresponding rank; determining a Pareto-set for the set of objective functions; with a Pareto-optimal point in the Pareto-set, determining a distribution over rankings for the objects in the set using the decision space; selecting a sequence of rankings for the set of objects from the distribution over rankings in accordance with their proportions; and outputting the selected sequence of rankings.

Abstract: A method of ranking items for a given entity uses sets of triplets <u, i, j>, each set of triplets including an entity u and a pair of items i and j with a known relative relevance for entity u, to train a learnable scoring function ƒ and to learn optimized values of a first set ? of learnable parameters. The training includes optimizing a loss function depending on ?, on a second set of learnable parameters ?g, and on a probability of having the item i preferred to the item j by the entity u. The probability defines a continuum between pointwise and pairwise ranking of items through a learnable mixing function depending on ?g. After training, the trained learnable scoring function ƒ is applied to all input pairs <u?, i?> to rank all items i? for an entity u?.

Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

Abstract: There is provided a computer-implemented method of locating points of interest for a user in a geographic area. A current state of the user is used as an index into a list of a plurality of current state functions to select at least one of the plurality of current state functions. Each current state function corresponds with a utility measure set where (i) each utility measure set includes a plurality of utility measures and (ii) the plurality of utility measures of each utility measure set is ordered in a selected sequence. The computer is used to perform lexicographic optimization with respect to the plurality of current state functions so that the selected sequence of each utility measure set is optimally ordered. In turn, the computer is used to rate a plurality of points of interest with respect to at least one of the plurality of optimized current state functions, and, based on the rating, to automatically select a set of points of interest for the user.

Abstract: A dialog device comprises a natural language interfacing device (chat interface or a telephonic device), and a natural language output device (the chat interface, a display device, or a speech synthesizer outputting to the telephonic device). A computer stores natural language dialog conducted via the interfacing device and constructs a current utterance word-by-word. Each word is chosen by applying a plurality of language models to a context comprising concatenation of the stored dialog and the current utterance thus far. Each language model outputs a distribution over the words of a vocabulary. A recurrent neural network (RNN) is applied to the distributions to generate a mixture distribution. The next word is chosen using the mixture distribution. The output device outputs the current natural language utterance after it has been constructed by the computer.

Abstract: An optimal diagnosis method chooses a sequence of tests for diagnosing a problem by an iterative process. In each iteration, a ranked list of hypotheses is generated or updated for each root cause. Each hypothesis is represented by a set of test results for a set of unperformed tests, and the generating or updating is performed by adding hypotheses such that the ranked list for each root cause is ranked according to conditional probabilities of the hypotheses conditioned on the root cause. The ranked lists of hypotheses for the root causes are merged, and a test of the set of unperformed tests is selected using the merged ranked lists as a proxy (i.e. a representative and sufficient sample) for the whole set of possible hypotheses. A test result for the selected test is generated or received. An update is performed, including removing the selected test from the set of unperformed tests and removing from the ranked lists of hypotheses those hypotheses that are inconsistent with the test result.

Abstract: A dialog device comprises a natural language interfacing device (chat interface or a telephonic device), and a natural language output device (the chat interface, a display device, or a speech synthesizer outputting to the telephonic device). A computer stores natural language dialog conducted via the interfacing device and constructs a current utterance word-by-word. Each word is chosen by applying a plurality of language models to a context comprising concatenation of the stored dialog and the current utterance thus far. Each language model outputs a distribution over the words of a vocabulary. A recurrent neural network (RNN) is applied to the distributions to generate a mixture distribution. The next word is chosen using the mixture distribution. The output device outputs the current natural language utterance after it has been constructed by the computer.

Abstract: A method for updating a predicted ratings matrix includes receiving an observation, the observation identifying a user, an item, an observed rating of the user for the item, and a time of the observation. Based on the observation, user and item latent factor matrices and user and item biases are updated using extended Kalman filters. The user latent factor matrix includes latent factors for each of a set of users and the item latent factor matrix includes latent factors for each of a set of items. A predicted ratings matrix is updated as a function of the user latent factor matrix and the item latent factor matrix. Recommendations can be generated using a sampling strategy based on a multi-armed bandit and the posterior distributions given by the extended Kalman filters.

Abstract: A system and method are disclosed which enable more effective email response authoring by contact center agents, for example, by automatically suggesting prototypical (entire) email responses to the human agent and interactive suggestion of next sentence candidates during the writing process. In one method, a customer inquiry is received and a latent topic prediction is generated, based on a word-based representation of the customer inquiry. A latent topic prediction is generated for an entire agent's reply to the customer inquiry as a function of the latent topic prediction generated for the customer inquiry. A further latent topic prediction is generated for a next sentence of the agent's reply as a function of a topic prediction for the next sentence which is generated with a prediction model that has been trained on annotated sentences of agent replies. Information is output to assist the agent, based on the topic predictions.

Abstract: A system and method are disclosed which enable more effective email response authoring by contact center agents, for example, by automatically suggesting prototypical (entire) email responses to the human agent and interactive suggestion of next sentence candidates during the writing process. In one method, a customer inquiry is received and a latent topic prediction is generated, based on a word-based representation of the customer inquiry. A latent topic prediction is generated for an entire agent's reply to the customer inquiry as a function of the latent topic prediction generated for the customer inquiry. A further latent topic prediction is generated for a next sentence of the agent's reply as a function of a topic prediction for the next sentence which is generated with a prediction model that has been trained on annotated sentences of agent replies. Information is output to assist the agent, based on the topic predictions.

Abstract: A method, system, and computer program product for extracting text motifs from the electronic documents is disclosed. A user provides a largest-maximal repeat or a super-maximal repeat as a first text block. The occurrences of the first text block are detected to identify the second text blocks in the vicinity of the occurrences of the first text block on the basis of pre-defined parameters. The text motifs are determined by combining the first text block and the second text block. Finally, the text motifs are extracted from the electronic documents.

Abstract: A system and method for selection of a batch of objects are provided. Each object in a pool is assigned to a subset of a set of buckets. The assignment is based on signatures, generated, for example, by LSH hashing object representations of the objects in the pool. The signatures are then segmented into bands which are each assigned to a respective bucket in the set, based on the elements of the band. An entropy value is computed for each of a set of objects remaining in the pool using a current classifier model. A batch of objects for retraining the model is selected. This includes selecting objects from the set of objects based on their computed entropy values and respective assigned buckets.

Abstract: A method for updating a predicted ratings matrix includes receiving one or more observations. Each observation identifies a user, an item and an observed rating of the user for the item. A user bias for the user and an item bias for the item are updated as a function of the rating. Based on the rating and the adapted user and item biases, at least one of a user latent factor matrix and an item latent factor matrix is updated. After the updating of the latent factor matrix or matrices for one of the observations, a predicted ratings matrix is updated as a function of the user latent factor matrix and the item latent factor matrix. The updating of the latent factor matrix or matrices may be performed for each new observation in a time series, so that the updated predicted ratings matrix reflects changes in user preferences and item popularities.

Abstract: A computer implemented tactile user interface (TUI) and a method of manipulating objects with a virtual magnet are provided. The TUI includes a display comprising a touch-screen. The display is configured for displaying a set of graphic objects, each graphic object representing a respective one of a set of items, such as documents, e.g., text documents or images. A virtual magnet is caused to move on the display, in response to touching on the touch-screen, e.g., by dragging a finger or other implement across. The magnet is associated with a particular function command such that a subset of the graphic objects exhibits a response to the virtual magnet (e.g., is caused to move, relative to the virtual magnet or exhibits another visible response), each graphic object in the subset moving or otherwise responding as a function of an attribute of the underlying item represented by the graphic object.

Abstract: A retrieval method on a database of documents including text and names of participants associated with the documents includes: receiving a text query facet of keywords and a persons query facet of participant names; computing an enriched text query as an aggregation of the text query facet, a monomodal expansion of the text query facet based on the keywords, a cross-modal expansion of the text query facet based on the participant names, and a topic expansion of the text query facet based on a topic model associating words and topics; computing an enriched persons query as an aggregation of the persons query facet, a monomodal expansion of the persons query facet based on the participant names, a cross-modal expansion of the persons query facet based on the keywords, and a community expansion of the persons query facet based on a community model associating persons and communities.

Abstract: A method and a system for coreference resolution are provided. The method includes receiving a set of document clusters, each cluster in the set of document clusters including a set of text documents. Instances of each of a set of candidate named entities are identified in the document clusters. For a pairs of the candidate named entities, at least one socio-temporal feature is computed that is based on the similarity of the distributions of identified instances of the respective candidate name entities among the document clusters. A decision for merging for the candidate named entities into a common real named entity is based on the socio-temporal features.