Abstract: An electronic device and a controlling method thereof are provided. The electronic device includes a memory and a processor configured to obtain importance data representing an importance of each of a plurality of words included in the text data using text data related to a plurality of items written by a plurality of users, obtain a topic model representing a relationship between a topic and a word by applying a topic modelling algorithm to the obtained importance data, obtain preference data representing preference of each of the plurality of users for the topic and relationship data representing a relationship between the plurality of items and the topic, based on the obtained topic model, and train a recommendation model to output result data including an estimated preference for the plurality of items of the plurality of users, based on the preference data and the relationship data.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: An electronic device is provided. The device includes a processor configured to receive, from a first external device, a list of a first number of items generated based on a request of a user, and a first score of each of the first number of items, identify a first relevancy between each element of a first feature set and each of the first number of items and a second relevancy between each element of the set and the user, identify a first list of a second number of items, identify, for at least one item of the first list, a third relevancy between a first feature of the set which satisfies a condition and the at least one item and a fourth relevancy between the first feature and the user, based on the first relevancy and the second relevancy, and output the third relevancy and the fourth relevancy with the first list.

Abstract: An electronic device is provided. The device includes a processor configured to receive, from a first external device, a list of a first number of items generated based on a request of a user, and a first score of each of the first number of items, identify a first relevancy between each element of a first feature set and each of the first number of items and a second relevancy between each element of the set and the user, identify a first list of a second number of items, identify, for at least one item of the first list, a third relevancy between a first feature of the set which satisfies a condition and the at least one item and a fourth relevancy between the first feature and the user, based on the first relevancy and the second relevancy, and output the third relevancy and the fourth relevancy with the first list.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: A method for optimising application program code including a plurality of fixed point variables. Each fixed point variable comprises a binary pattern. The binary pattern has an integer portion having an integer word length (IWL) and a fractional portion having a fractional word length (FWL). The method comprises simulating execution of the application to identify any variables to which values having a greater IWL are assigned and modifying the code to increase the IWL of those variables by an amount required to prevent overflow errors.

Abstract: Guidances are provided for database retrieval in a numerical simulation system. A user can execute numerical simulation without knowledge of mathematical and numerical techniques. A physical model is generated using information of a region shape, material name, physical phenomenon name, boundary condition name, and the like, respectively inputted by a user. A mathematical model generating step calculates physical characteristic values from the physical model, and in accordance with the physical characteristic values, retrieves proper equations from an equation database. A calculation model generating step detects mathematical characteristic values from the equations to retrieve proper numerical algorithms for solving the equations from an algorithm database. A simulation information generating step retrieves from a package database a simulation package for solving the given physical problem by using the selected algorithms, and generates simulation information for the retrieved package.