539results about How to "Improve recommendation efficiency" patented technology

The invention provides a method and a device for automatically recommending an application. The method comprises the steps of: receiving an application acquisition request submitted from a client by a user, wherein the application acquisition request comprises a user identification; according to the user identification, extracting the existing user behavior information of corresponding user from a user feature library, wherein the user behavior information comprises operation information that a user aims at previously recommended application; according to the user behavior information, determining the type of the application recommended to the user; in an application data set of the type of the application, according to the operation information that the user aims at the previously recommended application, extracting an matched application; and according to the type of the application, generating a corresponding application folder, and placing the matched application in the corresponding application folder for recommending. According to the invention, the individual needs of users can be satisfied, the recommending efficiency is improved and the coverage rate is increased.

The invention provides a news recommendation system and method based on an FOLFM model. Based on a content recommendation method, a news content model is expressed abstractly through a latent class model and content characteristics, and an individual latent class preference model is built for each user. Real-time training is carried out on a real-time behavior record of a user to obtain preference, on certain latent class news, of the user, whether the news is recommended to the user is determined through calculation, and a final news recommendation list is obtained after a series of processing processes. The news recommendation system and method based on the FOLFM model deeply excavate the interest of the user, improve recommendation accuracy and satisfaction of the user, avoid a cold starting problem of the news, and guarantee performance under the condition that the recommendation effect is improved as much as possible. The experiment shows that the news recommendation system and method based on the FOLFM model not only guarantee the requirements for high accuracy and high speed, but also realize visual real-time recommendation for the user.

The present invention is applicable to the technical field of computers, and provides a recommended method, apparatus and device, and a storage medium. The method comprises: obtaining history score data of a user, a to-be-scored item, and text content of the to-be-scored item; according to the history score data of the user, the to-be-scored item, and the text content of the to-be-scored item, training the preset stack noise reduction self-encoder and the preset probability matrix decomposition model to obtain the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and user latent characteristics; according to the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and the user latent characteristics, calculating the predicted score of the to-be-scored item; and according to the predicted score, generating a recommendation list, and outputting the recommendation list to the user, so that when recommending the item to the user, the item characteristics are combined with the user characteristics, the recommended accuracy is effectively improved, and the recommended efficiency of the item is further improved.

The invention discloses a content recommendation method and device and equipment. The embodiment of the method includes the steps that feature information, corresponding to multiple preset feature tags, of a user and to-be-recommended content are acquired; based on the feature information and recommendation weights, determined with the analytic hierarchy process, of the preset feature tags, recommendation indexes of the to-be-recommended content are calculated; target recommended content which is determined from the to-be-recommended content according to the recommendation indexes is recommended to the user, wherein the recommendation weights are determined in the way that the preset feature tags are clustered to determine class tags of the preset feature tags; a hierarchical structure model comprising a criterion layer, a sub-criterion layer and a target layer is constructed, wherein elements of the sub-criterion layer are the preset feature tags, and elements of the criterion layer are the class tags; based on the hierarchical structure model, weights, corresponding to the target layer, of the elements of the sub-criterion layer are determined with the analytic hierarchy process and serve as recommendation weights of the preset feature tags. By means of the embodiment, the recommendation efficiency can be improved.

The invention belongs to the load forecasting field of a power distribution system, relating to an auto recommending method of urban power load forecasting module based on associative rules. The method comprises the steps of: establishing a historical data base; carrying out data analysis and generalization; mining the associative rules; matching the rules; and obtaining model recommending conclusion by circulating the steps. The method not only can forecast the using condition of a model in an area to be measured, but also can conclude application rules of some models; by utilizing an inference method based on cases, the efficiency of model recommending is improved; and simultaneously the load forecasting efficiency is improved by combining certain expertise.

The invention discloses a network community based collaborative filtering recommendation method which mainly solves the problem that a recommendation accuracy rate is low in the prior art due to sparsity in acquisition of similarity data of users. The network community based collaborative filtering recommendation method includes: acquiring rating information for recommended items from users and generating a user relation network among the users indirectly through the rating information for the recommended items from the users; computing similarity among the users; partitioning the user relation network into multiple user communities via similarity-based community detection; selecting k users, with the largest similarity, from a local community of the user to form a neighbor user set, and predictively rating items not rated by target users according to the neighbor user set; recommending the item rated with the highest prediction value to the user. From a result of a simulation experiment, the network community based collaborative filtering recommendation method can obtain a better recommendation result as compared with a conventional collaborative filtering recommendation method, and can be used for recommending the items the users interested in to the users.

The invention discloses an information push method and device and belongs to the technical field of internet. The method comprises obtaining historical behavior data of users towards current books, wherein the historical behavior data comprises book purchasing data, book searching data and online book reading data of the users; calculating popularity scores of the current books and preferences of the users towards preset labels according to the user historical behavior data respectively; obtaining books to be recommended to the users according to the current book labels, popularity scores and the preferences of the users towards preset labels. According to the method and the device, personalized recommendation is performed through analysis of the user historical behavior data, manual editing is not required, and the web portal recommendation efficiency is improved.

The invention relates to a commodity recommendation method and device, and belongs to the field of the network technology. The method comprises the following steps of on the basis of a plurality of first user amounts, determining a commodity recommendation list, wherein each first user amount is the amount of users who purchase first-category commodities and execute an appointed behavior for each second-category commodity in a plurality of second-category commodities in a preset time period, and the commodity recommendation list comprises N second-category marks; for each second category in N second categories, on the basis of the mark of the second category, determining the marks of the plurality of commodities which belong to the second category; on the basis of target user characteristic information and the commodity characteristic information of the plurality of commodities which belong to the second category, determining a plurality of recommended purchase probabilities through an appointed logistic regression model; and on the basis of the plurality of recommended purchase probabilities, recommending a target commodity in the plurality of commodities which belong to the second category to the target user. Therefore, different commodities are recommended to different users in a targeted way, and commodity recommendation efficiency is improved.

The invention discloses a method for recommending documents based on tags and a document recommending device. The method comprises the following steps of: receiving a document tag selected by a user, searching a previously set synonym word collection and obtaining a synonym group corresponding to the document tag; according to the obtained synonym group, searching and obtaining a document list containing the synonym group from a previously stored document library; calculating the characteristic value of each document in the document list containing the synonym group, and constructing a space weight vector based on the document tag; and according to the constructed space weight vector based on the document tag, calculating the similarity of documents corresponding to the document tag with each document in the document list, selecting a preset number of documents with the highest similarity as searched results and outputting the searched results. Through the application of the method and the device, the document recommending efficiency can be increased.

The invention discloses an information recommendation method and device. The method includes the steps of obtaining the target position region where a user is currently located, and determining the recommendation mode according to the target position region, wherein the recommendation mode is adapted to the requirements of the user in the target position region; generating a recommendation result corresponding to the recommendation mode; outputting the generated recommendation result. According to the information recommendation method and device, the recommendation mode can be determined according to the target position region where the user is currently located, and the recommendation mode is adapted to the requirements of the user in the target position region, so that the recommendation result obtained according to the recommendation mode can provide more effective information for the user, and recommendation efficiency is improved.

The invention provides a method and device for automatic recommendation application. The method includes the steps of collecting user access information, dividing categories which the user access information belongs to, searching for matched applications in an application data set of preset corresponding categories according to the user access information and the categories of the user access information, generating application files corresponding to the categories and putting the searched applications of the categories in the corresponding application files to carry out recommendation. According to the method and device, individualized demands of users can be met, and recommendation efficiency and the coverage rate are improved.

The invention discloses an information recommendation method and device used for sending recommended information to a user timely and accurately. The method includes: acquiring position information of the user; matching with a pre-established user model according to the position information, and determining feature information of the user; sending the recommended information to the user according to the feature information. By the method and device, the corresponding recommended information is sent to the user according to the feature information, related features of the user are determined according to the user model, further related information more adaptable to the feature information is recommended to the user, and accuracy of the recommended information is improved; besides, as the user model is established in advance, the feature information of the user can be returned quickly through the user model so as to send the recommended information to the user timely and accurately, and recommendation efficiency is increased.

The invention relates to a recommendation sorting method based on a wide and deep gate loop joint model, and belongs to the technical field of natural language processing. According to the method, Sina Weibo data are first crawled for preprocessing to obtain a topic feature set; then generalized cross-feature conversion is used to memorize topic features, and the same is input into a linear module; then an embedding vector is learned for each classification feature, all the embedding vectors are connected with dense features, dense vectors generated by connection are input to a deep module formed by gate loop units; and finally, parameters in linear and deep loop processes are simultaneously optimized, and a recommendation sorting result is obtained through joint training on the model. According to the method, the gate loop units are used for feature generalization, the problem that conventional methods mostly do not consider sequence features of dynamic time sequences is alleviated, abetter recommendation result is achieved as a whole, and recommendation efficiency is also improved to a certain extent.

The embodiment of the invention discloses a news recommending method and a news recommending device based on artificial intelligence. The news recommending method comprises the following steps: acquiring a first news characteristic of news to be recommended, and a second news characteristic of browsed news; according to the first news characteristic and the second news characteristic, determining whether the news to be recommended is the same as the browsed news or not; if the news to be recommended is the same as the browsed news, refusing to recommend the news to be recommended; if the news to be recommended is not the same as the browsed news, recommending the news to be recommended. According to the embodiment of the invention, as whether the news to be recommended is the same as the browsed news or not is determined, and only different pieces of news are recommended to a user, the phenomenon that same news is recommended to the user repeatedly can be effectively avoided, and the news recommendation efficiency can be improved.

The invention relates to the technical field of artificial intelligence, and discloses an information recommendation method and device based on a knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining initial data, recognizing the relationship between entities in the initial data, and constructing the knowledge graph; when a target customer is determined, extracting initial data of the target customer from the knowledge graph, constructing a sub-graph, then training a graph convolutional neural network GCN by adopting the sub-graph and a pre-constructedproduct feature vector, inputting product information data in basic data into the trained graph convolutional neural network GCN, and carrying out binary classification processing; and obtaining a selection probability of each product, selecting the corresponding product information data as to-be-recommended information according to the selection probability, and pushing the to-be-recommended information to the target client. The invention also relates to blockchain technology. Initial data is stored in the blockchain. According to the invention, the knowledge graph is constructed to improve the efficiency of information recommendation.

The invention relates to the field of network technologies, in particular to a music recommendation method and system. The method applied to the music recommendation system includes: receiving a music recommendation request; judging whether a user corresponding to the music recommendation request is a new user or not; if yes, acquiring behavior data of the user and/or first music resource data related to the user by at least one external means, processing the behavior data of the user and/or first music resource data related to the user acquired by at least one external means, classifying the user to acquire the level of the user according to processing results, and entering a step of recommending personal music by a recommendation method corresponding to the level according to the level of the user; if the user is not new user, acquiring the corresponding level of the user; recommending personal music by a recommendation method corresponding to the level according to the corresponding level of the user.