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

The invention discloses a content recommendation method and device based on user favorites. The method mainly includes the steps that feature word extraction is performed according to collected behavior data of all users, and extracted feature words are respectively matched with preset categories and / or entity word banks to construct a favorite topic library of the categories and / or entity words corresponding to the behavior data of the users; according to multi-dimension attribute information corresponding to the behavior data of the users, the categories and / or the entity words in the favorite topic library of the users are sequenced; before recommended content are displayed for the users, a preset recommended content library is searched for, and sequenced recommended content which are matched with the sequenced categories and / or the entity words in the favorite topic sequencing library are extracted in advance; when the users give access requests, current access network scenes of the users are extracted, and the sequenced recommended content matched with the current access scenes of the users are extracted to be displayed. According to the technical scheme, recommending efficiency can be further improved.

The invention provides a method and a device for automatic application recommendation. The method includes the steps: collecting behavioral information of a user; categorizing the user behavioral information; according to the user behavioral information and categories of the same, searching matched applications from application data sets of preset corresponding categories; and generating application files corresponding to all the categories, and placing the searched applications of all the categories into the corresponding files for recommendation. The method is capable of meeting personalized needs of the user and improving recommendation efficiency and coverage rate.

Embodiments of the invention provide a personalized video recommendation method and apparatus. The method comprises the steps of obtaining a user portrait characteristic corresponding to a user identifier; according to the user portrait characteristic, obtaining a video characteristic model corresponding to the user portrait characteristic; and according to the video characteristic model corresponding to the user portrait characteristic, obtaining a video matched with the video characteristic model corresponding to the user portrait characteristic from a video database and performing recommendation. According to the method and apparatus, a user can quickly and accurately obtain the video interested by the user, so that the video acquisition efficiency is improved, the personalized video demand of the user is met, and the experience of user video search and recommendation is greatly enhanced.

The invention discloses item recommendation method and system based on a user-item bipartite model. The method includes: extracting acquired user behavior information to establish a user-item bipartite model; establishing an item-to-user inverted list model on the basis of the user-item bipartite model, calculating item weights, and establishing a user-item weight list; calculating weights of item pairs corresponding to users and common user weights of the item pairs according to the established user-item weight, and establishing an item pair-common user weight list; operating to obtain an inter-item similarity list according to a preset similarity algorithm; querying the established user-item bipartite model to obtain recommended user-mapped items, querying an inter-item similarity list according to the user-mapped items to be recommended to obtain query result, and generating a recommendation list according to the query result. By the use of the method and system, calculation needed by recommendation can be reduced and recommendation efficiency can be improved.

The invention relates to a code recommendation method based on a long short-term memory (LSTM) network. For the problem that low recommendation accuracy rates, low recommendation efficiency and the like are ubiquitous in existing code recommendation technologies, the method firstly extracts source code to form an API sequence, utilizing the long short-term memory network to build a code recommendation model to learn relationships between API calls, and then carries out code recommendation. A dropout technology is used to prevent model overfitting. At the same time, using a ReLu function to instead a traditional saturation function is provided, the gradient vanishing problem is solved, a model convergence speed is accelerated, model performance is improved, and advantages of the neural network are fully exerted. The technical scheme of the invention has the characteristics of simpleness and quickness, and can better improve an accuracy rate and recommendation efficiency of code recommendation.

The invention is applicable to the field of a social network, and provides a user interest recommending method and a user interest recommending device. The method comprises the steps of acquiring user interest label information according to UGC (user generated content) of the social network; clustering users with the interest labels of the same category for forming a cluster according to the acquired interest label information; recommending the interest labels of the users in the same cluster to the users in the cluster, or mutually recommending the users in the same cluster to be friends with the same interest. The interest labels of the users are acquired from the UGC, so that the interest label matching accuracy is high, users in the cluster based on high accuracy are subjected to interest label recommendation or friend recommendation, the recommendation accuracy is high, the recommending efficiency is favorably improved, and the user interest label can be further improved.

The invention provides a face identification-based personalized video recommendation method and recommendation system. The method comprises the steps of acquiring a face image of a user and extracting and storing the face features in the face image of the user; acquiring network video data and performing feature vector extracting on the network video data and then generating video feature vectors; generating a user interest model according to the video feature vectors and user behavior data corresponding to the face features; calculating the similarity degrees of the user interest model and the video feature vectors, and forming a recommendation list based on the calculated similarity degrees. The method and the system can analyze the interest and preference of a user after identifying the face of the user, can filter video with low similarity degrees and video that has been viewed already in to-be-recommended video, are high in recommendation efficiency and provide convenience for users.

The invention discloses an application recommendation method and device and server equipment. The method comprises the steps that according to a behavior data list of application acquisition terminal equipment recently browsed and downloaded by terminal equipment, the list comprises a plurality of first application identifications and nearest behavior time of first applications; according to the degree of association between the first applications and second applications in a preset application bank, multiple corresponding second applications are obtained from the preset application bank to form an alternative application assembly; according to current time, the nearest behavior time of the first applications is unified to obtain initial weighted values of the first applications; according to the degree of association of the second applications, the first application identifications corresponding to the degree of the associations and the initial weighted values of the first applications, an application recommendation list is generated, the application recommendation list is sent to the terminal equipment to allow the terminal equipment to show the application recommendation list. According to the technical scheme, individualized recommendation can be achieved, and recommendation efficiency of the applications is effectively improved.

The invention provides a rating prediction based project recommending method for time-sequence control and a system thereof. The corresponding project recommending method comprises the following steps: inputting a project to be recommended; determining a time-sequence rating model related to the project, wherein the time-sequence rating model is used for predicting rating changes of the project along with time; applying one or more recommending policies to the determined time-sequence rating model to determine a preference recommending time of the project; and recommending the project for a user at the determined preference recommending time. In different embodiments, the time-sequence rating model of the project can be selected from a pre-saved time-sequence rating model set, or is automatically generated according to historic data of the system. Furthermore, the selected time-sequence rating model can be adjusted according to user preferred information or feedback information. The project recommending system considers that the interest of users in recommended projects changes along with time so as to increase the recommending efficiency and improve user experience.

The embodiment of the invention provides an information recommendation method and device. The method comprises the steps that historical operation behaviors of users are recorded; content preference tags are generated according to the historical operation behaviors; a neighbor user meeting a preset similarity condition with a target user is determined according to the content preference tags; historical operation behavior data of the neighbor user is acquired; and information recommendation is made to the target user according to the historical operation behavior data of the neighbor user.

The invention discloses a direct broadcasting room recommending method and system based on broadcaster styles, and relates to the network technical field; the method comprises the following steps: collecting characteristic parameters and user data of direct broadcasting rooms from a server in a set time period; using characteristic parameters of each direct broadcasting room as characteristic constants to build a characteristic vector of the direct broadcasting room; selecting two random direct broadcasting rooms with broadcasters of different personal information, calculating similarity between characteristic vectors of the two direct broadcasting rooms, and determining direct broadcasting rooms with similarities; recommending other direct broadcasting rooms similar to the direct broadcasting room to all users in the direct broadcasting room according to user data of each direct broadcasting room; calculating a characteristic vector evaluate index according to the visiting rate and / or return visiting rate of the recommended direct broadcasting room, using the evaluate index to screen the characteristic vector characteristic constant, and using the screened characteristic vector to determine similar direct broadcasting rooms. The method and system can precisely recommend direct broadcasting rooms with similar styles to users, thus improving recommending efficiency, and improving user experiences.

The invention discloses a personalized recommendation method based on the cloud processing mode and applied in the e-business environment. The personalized recommendation method mainly solves the problem that an existing personalized recommendation method is low in recommendation efficiency and poor in recommendation precision when processing mass data. The personalized recommendation method is divided into an off-line portion and an on-line portion. According to the off-line portion, the Hadoop frame of the cloud computing technology is used for parallel processing of historical data information, an HDFS is used for storing mass data information, and four kinds of parallelized recommendation methods which are suitable for different business stages of e-business are achieved according to the MapReduce programming model. According to the on-line portion, a lightweight data base is arranged and used for storing a user behavior log, a dynamic data collection mechanism is designed and used for reading data which are processed and obtained by the off-line portion in real time, web display and information statistics service are provided, and real-time recommendation information is provided for a user. The personalized recommendation method based on the cloud processing mode and applied in the e-business environment has the remarkable advantages of processing the mass data generated by e-business application.

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.

This invention puts forward an information recommend system and a method based on Fuzzy logic, which applies fuzzy integration to store said user file, matches the file with received information by Fuzzy logic inference mode, selects information in conformity with the interest of the user and recommends it to the user after sequencing based on the degree of interest. The user can refresh its file timely based on its fed-back information. The system can simulate thought of mankind so as to increase the satisfaction degree and effectiveness of information recommendation.

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 provides a method for automatically completing codes on the basis of LSTM (Long Short Term Memory). The method comprises the following steps: performing source code processing, and analyzing source codes by using an abstract grammar tree; performing off-line training, and training a language model by using an LSTM model; performing on-line code completion, and completing codes according to the trained language model. The LSTM model comprises constraint character level LSTM and mark character level LSTM with former context marking character encodes. By adopting the method, automatic code completion can be achieved by inputting any character into any place in the encoding process, recommendation of any code can be also achieved, and the recommendation process accuracy can be also ensured. The technical scheme of the invention has the characteristics of being simple and rapid, and the code recommendation accuracy and the recommendation efficiency can be relatively well improved.