Abstract: A method for matching cross-area products includes steps as follows. First and second local product lists are matched through text similarity and graph similarity, and a corresponding relation of the matched first and second products is built. A first difference of topic probability vector of the first and second products and a second difference of topic probability vector of third and fourth products are calculated. If the first difference of topic probability vector is similar to the second difference of topic probability vector, the third and fourth products that are failed to be matched are built a corresponding relation. A cross-area product list of the first and second local product lists is generated. First and second local electronic commerce product lists are added in the first and second local area lists. The first and second local area lists corresponding to the cross-area product list are displayed on a displaying device.

Abstract: Embodiments disclosed relate to a computer device and a method for predicting market demand of commodities. The method includes: creating multiple-sources data for each of a plurality of commodities, wherein each of the all multiple-sources data comes from a plurality data sources; storing the all multiple-sources data; extracting a plurality of features from a corresponding one of the all multiple-sources data for each of the commodities to build a feature matrix for each of the data sources; performing a tensor decomposition process on the feature matrices to produce at least one latent feature matrix; and performing a deep learning process on the at least one latent feature matrix to build a prediction model and predicting market demand of each of the commodities according to the prediction model.

Abstract: A recommendation method includes providing an ontology database, in which the ontology database includes a plurality of entities, and the entities are arranged in an ontology hierarchy structure with N hierarchy levels; storing a plurality of jth level user data respectively corresponding to a plurality of users; generating a plurality of kth level user data according to the jth level user data respectively; clustering the kth level user data; and recommending the entities in the ontology database to the users according to a clustering result.

Abstract: An electronic calculating apparatus, a method and a non-transitory machine-readable medium are provided. The electronic calculating apparatus includes an input/output (I/O) interface, a knowledge tree database and a processor. The I/O interface receives first data of a user, wherein the first data is comprised of a natural language character string. The knowledge tree database stores a context knowledge tree. The processor receives the first data of the user via the I/O interface, analyzes the first data and generates context characteristic information, and substitutes the context characteristic information into the context knowledge tree to generate first context recommend information. The processor then enables a display apparatus to display the first context recommend information.

Abstract: An electronic computing device, a personalized information providing method thereof, and a non-transitory machine-readable medium thereof are provided. The electronic computing device establishes a first and a second tree structure data according to a first data of a first user and a second data of a second user arranged in a period respectively by using an ontology construction algorithm, and calculates a similarity between the first and the second tree structure data by using a similarity evaluating algorithm, and then analyzes the similarity to subsume the first and the second tree structure data into a group by using a clustering algorithm. The electronic computing device determines difference between the first and the second tree structure data according to the group and generates recommending information corresponding to the first user which is arranged in the period according to the difference, and then enables a monitor to display the recommending information.

Abstract: A merchandise recommendation method for multiple users used in a merchandise recommendation system including a user database, a merchandise database, a data transmission module, a processing module and a memory is provided. The merchandise recommendation method includes the steps outlined below. The processing module receives participant information and target merchandise information from a remote originator host. The processing module retrieves corresponding user information from the user database according to the participant information. The processing module retrieves corresponding merchandise information from the merchandise database according to the target merchandise information. The processing module analyzes social influence information and preference information included in the user information and analyzes the merchandise information to generate an analysis result. The processing module generates composite merchandise recommendation information according to the analysis result.

Abstract: A recommendation method includes providing an ontology database, in which the ontology database includes a plurality of entities, and the entities are arranged in an ontology hierarchy structure with N hierarchy levels; storing a plurality of jth level user data respectively corresponding to a plurality of users; generating a plurality of kth level user data according to the jth level user data respectively; clustering the kth level user data; and recommending the entities in the ontology database to the users according to a clustering result.

Abstract: An itinerary generation apparatus, method, and non-transitory computer readable storage medium thereof are provided. The itinerary generation apparatus includes a storage unit, an interface, and a processing unit, wherein the processing unit is electrically connected to the storage unit and the interface. The storage unit is stored with a piece of information related to a place. The interface is configured to receive a plurality of images, wherein each of the images has a shoot time. The processing unit determines that a portion of the images corresponds to the place according to a piece of schedule information. The processing unit retrieves the piece of information related to the place from the storage unit after determining that the portion corresponds to the place.

Abstract: An electronic computing device, a personalized information providing method thereof, and a non-transitory machine-readable medium thereof are provided. The electronic computing device establishes a first and a second tree structure data according to a first data of a first user and a second data of a second user arranged in a period respectively by using an ontology construction algorithm, and calculates a similarity between the first and the second tree structure data by using a similarity evaluating algorithm, and then analyzes the similarity to subsume the first and the second tree structure data into a group by using a clustering algorithm. The electronic computing device determines difference between the first and the second tree structure data according to the group and generates recommending information corresponding to the first user which is arranged in the period according to the difference, and then enables a monitor to display the recommending information.