Abstract:
A method and an apparatus for fusing position information, and a non-transitory computer-readable recording medium are provided. In the method, words of an input sentence are segmented to obtain a first sequence of words in the input sentence, and absolute position information of the words in the first sequence is generated. Then, subwords of the words in the first sequence are segmented to obtain a second sequence including subwords, and position information of the subwords in the second sequence are generated, based on the absolute position information of the words in the first sequence, to which the respective subwords belong. Then, the position information of the subwords in the second sequence are fused into a self-attention model to perform model training or model prediction.
Abstract:
A recommendation method and a recommendation apparatus based on deep reinforcement learning, and a non-transitory computer-readable recording medium are provided. In the method, entity semantic information representation vectors of products are generated based on a product knowledge graph; browsing context information representation vectors of the products are generated based on historical browsing behavior of a user with respect to products; the entity semantic information representation vectors and the browsing context information representation vectors of the respective products are merged to obtain vectors of the products; a recommendation model based on deep reinforcement learning is constructed, and the recommendation model based on the deep reinforcement learning is offline-trained using historical behavior data of the user to obtain the offline-trained recommendation model, the products in the historical behavior data of the user are represented by the vectors of the products; and products are online-recommended using the offline-trained recommendation model.
Abstract:
An intention identification method includes generating a heterogeneous text network based on a language material sample; using a graph embedding algorithm to perform learning with respect to the heterogeneous text network and obtain a vector representation of the language material sample and a word, and determining keywords of the language material sample based on a similarity in terms of a vector between the language material sample and the word in the language material sample; training an intention identification model until a predetermined training termination condition is satisfied, by using the keywords of the language material samples, and obtaining the trained intention identification model; and receiving a language material query, and using the trained intention identification model to identify an intention of the language material query.
Abstract:
User behavior analysis method and device are disclosed. The method comprises: obtaining a behavior record of users regarding commodities; acquiring, by determining a commodity feature vector in a predetermined commodity feature space of each of the commodities, a commodity feature matrix consisting of the commodity feature vectors of the commodities; for each of the users, calculating, based on a sub behavior record of the corresponding user regarding each of the commodities, a preference score of the corresponding user regarding each of the commodities, so as to get a score matrix composed of the preference scores of the users regarding the commodities; and for each of the users, determining, based on a regularized least squares based solution of a difference function of the score matrix and a prediction matrix, a user feature vector of the corresponding user in its related predetermined commodity feature space.
Abstract:
A method, an apparatus and a system for recognizing an evaluation element are provided. The method includes receiving an input text; performing, using a first conditional random field model, first recognition for the input text to obtain a first recognition result, the first recognition result including a pre-evaluation element that is recognized by using the first conditional random field model; performing, using a second conditional random field model, second recognition for the input text to obtain a second recognition result, the second recognition result including a false positive evaluation element that is recognized by using the second conditional random field model, the false positive evaluation element being an element erroneously detected as an evaluation element; and recognizing, based on the first recognition result and the second recognition result, an evaluation element in the input text.
Abstract:
The embodiments provide a cloud brainstorming service implemented on at least one cloud server. The brainstorming service includes a message service component configured to receive a plurality of ideas, over a network, from one or more users of devices. The users represent members of a brainstorming session. The brainstorming service also includes a brainstorming logic component configured to process the plurality of ideas and store the plurality of processed ideas in an in-memory database system, and a clustering component configured to retrieve the plurality of processed ideas from the in-memory database system and arrange the plurality of processed ideas into one or more clusters, where each cluster is a group of similar ideas. The message service component is configured to provide the plurality of processed ideas that are arranged into the one or more clusters, over the network, to the one or more users for display.
Abstract:
A request handler may receive transaction requests for transactions to be executed using data of a database, and may classify a first transaction request of the transaction requests as a simple transaction request, and a second transaction request of the transaction requests as a complex transaction request. A key-value store engine may execute a first transaction satisfying the first transaction request, using a key-value store of pre-calculated results determined prior to receipt of the first transaction request, and based on the data, and may update a key-value delta reflecting a change, if any, of the key-value store caused by the first transaction. A relational store engine may cause the at least one processor to execute a second transaction satisfying the second transaction request, using a relational store including a subset of the data, and may update a relational delta reflecting a change, if any, of the relational store caused by the second transaction. A synchronizer may execute a synchronization of the key-value store and the relational store, based on the key-value delta and the relational delta.
Abstract:
A computer system includes at least one processor and at least one memory operably coupled to the at least one processor. The memory includes a memory pool and a database partitioned into multiple fragments. Each of the fragments is allocated a block of memory from the memory pool and the fragments store compressed data in a columnar table format. A database operation is applied in a compressed format to the compressed data in at least one of the fragments.
Abstract:
The method includes determining at least one business objective on which to base a recommendation list for a first item, associating a configurable target with the business objective, the configurable target being based on a goal for a second item, determining at least one business constraint relating the first item with the second item, the at least one business constraint being based on the business objective and the associated configurable target and generating the recommendation list for the first item based on a list of candidate items and the business constraint.
Abstract:
The invention relates to a hybrid integrated wind-solar-diesel-city power supply system, which comprises at least one subsystem selected from wind power subsystems or solar power subsystems and at least one diesel-city power subsystem, a direct-current bus unit, a main control unit, multiple high frequency rectifiers and a direct-current distribution unit. Each one subsystem has a DC output coupled to said direct-current bus unit for afflux. Said main control unit is configured to select a set of subsystems from the wind and solar power subsystems and enable the selected set of subsystems but disable others, so as to let a sum of maximum power output of all enabled subsystems to be larger than or equal to power demanded while minimize the number of the enabled subsystems contained in the selected set of the subsystems, and adjust operation of the selected subsystems so as to optimize the system efficiency, and also configured to control current and voltage output of said high frequency rectifier according to the operation status of said direct-current distribution unit and the voltage and current output of said direct-current bus unit, thereby advantageously increasing efficiency and reducing the power consumption of the system, and thus also improve reliability and life of the system apparatus.