133results about How to "Realize personalized recommendation" patented technology

The invention relates to a commodity recommendation method in electronic commerce and a system using the same. The method comprises the steps of firstly, collecting historical behavior data in an electronic commerce website of a user; secondly, performing commodity feature calculation according to the historical behavior data of the user and outputting commodity purchase probability prediction feature vectors of the user; thirdly, placing the output commodity purchase probability prediction feature vectors of the user in a statistical function according to feature and classification and calculating a user purchase probability prediction model to obtain a purchase probability prediction model; fourthly, performing commodity recommendation according to the purchase probability prediction model for the user and user login information. According to the invention, commodities meeting the requirements of users are recommended, personalized recommendation is realized, the customer satisfaction is improved and the user experience is improved.

The invention discloses a personalized network learning resource recommendation method which comprises the following steps: analyzing behavioral data of learners for visiting a network leaning system based on expanded theme pictures, acquiring leaning interest path changing patterns of the learners and groups of the learners on concepts and knowledge elements related to learning contents, and then realizing active personalized recommendation of proper learning resources to the learners according learning interest path changing patterns of the learner individuals and the groups of the learners individuals, sequence of learning objects of the expanded theme pictures and the like. The personalized network learning resource recommendation method can provide personalized learning service for network learners, thereby improving learning efficiency of the network learners.

The invention relates to a multidimensional individualized recommendation method in a heterogeneous network which includes: 1, obtaining information; 2, establishing a similarity matrix between the user and item; 3, establishing a semi-structured heterogeneous information network; calculating the degree of correlation between users and items and between users and users under different meta paths based on the semi-structured heterogeneous information network; distributing different weights to the degree of correlation of each meta path to form a similarity matrix between users and other types of entities in the semi-structured heterogeneous information network; 4, distributing different weights and integrating the degree of correlation between users and items and between users and users with user's preference to items to form the final similarity matrix; 5, recommending a plurality of items with big similarity in the final similarity matrix to users. The multidimensional individualized recommendation method is added with user subordinate information and item subordinate information, considers the rich semantic information between users and items, and improves the recommendation accuracy and percentage of coverage.

The present invention discloses a mobile game recommendation system and a recommendation method thereof. The system comprises: a user use behavior acquisition module for acquiring dynamic behavior information and historical use behavior information of a current user based on behavior logs of game use of the current game user; a user behavior analysis module for analyzing user behaviors namely analyzing the use behaviors of the current user and 'friends' associated with the current user to calculate the user similarity and find out similar user groups; a game application list generation module for clustering games into multiple clusters according to labels, keywords and game brief introduction of game application information and description of user evaluations and the like, and generating a similar game application list for each game; and a game recommendation module for generating a recommendation list by using similar game application lists, integrating a recommendation based on a game application and a recommendation result based on a game user in a weighting manner by selecting appropriate weights to form a final recommendation result, setting corresponding recommendation thresholds according to a game application recommendation quantity, and recommending games not installed by the user to the user for installation and use.

The invention discloses a hybrid clustering recommendation method based on Gaussian distribution and distance similarity. The hybrid clustering recommendation method includes the following steps: S1, acquiring a user behavior data set, dividing the behavior data set into labeled data and no-label data, and performing distance metric learning on the labeled data and the no-label data respectively; S2, according to a distance metric weight matrix and a Gaussian hybrid model, performing hybrid calculation to acquire a target function, and performing optimized solving on the target function; S3, after acquiring an optimized solution of the target function, acquiring clustering behavior data through a clustering algorithm, and recommending clustering behavior data to users.

The invention discloses a search recommendation method which includes: receiving the search word of a user, and acquiring the identification of the user; acquiring of the category of the search word; acquiring the recommendation model corresponding to the user according to the identification of the user; inquiring the recommendation model to obtain a recommendation result according to the search word and the category of the search word; providing the recommendation result in a search result page. The method has the advantages that the recommended contents are related to user behavior features, that is to say, different cards are recommended to users with different behavior features, personalized recommendation of each user is achieved, the potential user demands of the users can be maximally stimulated, and user experience is increased. The invention further discloses a search recommendation device.

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 discloses an intelligent course selection recommendation method. According to the intelligent course selection recommendation method, a course map tree is established by calculating the similarity with the current student course selection page access, the similarity with the current student course grade, the similarity with the current student course selection record, recommended course selection sets respectively are obtained, then the obtained course selection sets are combined in a weighting mode to obtain the final recommended course selection set. A plurality of dimensionalities are taken into consideration, individual recommendation of courses is achieved, and blindness of student course selection is effectively solved. Existing system data of a school are utilized very well, mature collaborative filtering and the recommendation technology based on the content are utilized, and the method is easy to achieve.

The invention specifically relates to an individualized learning-oriented educational resource recommendation method and system. The method comprises the following steps of: S1, acquiring basic characteristic data of learners and establishing a resource interestingness model and a learning style model of the learners according to the basic characteristic data, wherein the basic characteristic data comprises identity information, learning behavior records and educational resource operation records of the learners; S2, establishing a learner characteristic model according to the resource interestingness model and the learning style model; S3, calculating the similarity between different learners or the similarity between different educational resources according to the learner characteristic model; and S4, generating a target learner-oriented educational resource recommendation result according to the similarity between different learners or the similarity between different educational resources. Through the technical scheme provided by the invention, the target learner-oriented education resource individualized recommendation can be realized.

The invention discloses a personalized pushing method based on user reading preference statistics of reading time. The method includes following steps: step 1, establishing calculating standards of average reading time of electronic reading materials; step 2, judging reading behaviors of a user; step 3, establishing average reading speed of the user; step 4, calculating reading time for a single article of the user; step 5, judging reading completeness according to actual reading time and the average reading time of the user; step 6, establishing preference degree of the user according to the reading completeness of the user; step 7, performing matching similarity operation on user preference vector and article content feature vector to acquire interest evaluation value of the user on an un-read article, and recommending a series of articles with high interest evaluation value to the user. The preference degree of the user is judged mainly according to article reading time and text information of the user, and then articles in which the user has interest are recommended to the user.

The invention belongs to the technical field of website recommendation, and provides a hybrid recommendation system oriented to a local chronicles website. The hybrid recommendation system comprises a behavior recording module, a user classifying module and a user recommendation module; the behavior recording module is used for recording user registration information and behavior information and storing the user registration information and behavior information into a database; the user classifying module is used for dividing users into new users, general users and active users according to the local chronicles access frequency of the users, conducting feature analysis on each user class to select the suitable recommendation method to achieve personalized recommendation; the user recommendation module comprises TopN recommendation, association rule recommendation based on local chronicles labels and improvement and collaborative filtering recommendation based on the users. The system adopts different recommendation algorithms for users of different type according to the classifying result of the user classifying module. Personalized recommendation can be conducted according to different user types.

The invention relates to the technical field of information, in particular to a product quality tracing and anti-fake system on the basis of obvious codes and secret codes. The product quality tracing and anti-fake system comprises two-dimension codes, a terminal query device and a product information platform, wherein the two-dimension codes include a tracing code and an anti-fake code, the product information platform includes basic product information, the two-dimensional codes and a product are bound together, the terminal query device uploads two-dimensional information and user data to the product information platform via reading and analyzing the two-dimensional codes, the product information platform queries and verifies the received data and returns tracing information and anti-fake information of the product to the terminal query device, and the terminal query device is communicated with the product information platform via a wireless network. The product quality tracing and anti-fake system on the basis of the obvious codes and the secret codes has the advantages that traceability is strong, a full process including all links from production to circulation to a consumer can be traced; individual recommendation is realized; and the product quality tracing and anti-fake system is convenient and quick to utilize, can realize querying in one step since the basic product information, quality detection information and product recommendation are integrated.

The invention discloses a recommendation method and system for application extension tools. The method includes: collecting utilization information about installed extension tools applied by a user; analyzing the collected utilization information to determine user's preference degrees on the installed extension tools, and selecting the first extension tool with the preferred degree reaching a first preset condition; from the user's uninstalled extension tools, selecting the second extension tool having the similarity, to the first extension tool reaching the first preset condition, reaching a second preset condition, and recommending the second extension tool to the user. The recommendation method and system has the advantages that personal recommendations on extension tools can be provided for users and effective utilization rate of the extension tools can be increased.

The invention provides a commodity recommendation method and device, an electronic commerce system and a storage medium, and relate to the technical field of electronic commerce. The method comprises the steps: receiving a commodity recommendation request sent by a client, wherein the commodity recommendation request comprises a commodity class, first user position information, and the number N of target groups; screening out N target users based on the first user position information, wherein the commodities purchased by the target users comprise to-be-selected commodities matched with the commodity class; screening out the recommended commodities from the to-be-selected commodities according to a recommendation rule, and transmitting the commodity recommendation information corresponding to the recommended commodities to the client. According to the invention, the method achieves the personalized recommendation based on the number of groups nearby a user, and the user can dynamically select the recommendation range of groups. The method can obtain the commodities needed by the user more flexibly, improves the online shopping efficiency, and improves the purchase experience of the user.

The invention discloses a partial model weight fusion Top-N film recommending method based on user clustering. The method comprises the steps of 1, preprocessing data, wherein inactive users and filmswith very low popularity are subjected to data cleaning, user film label documents are constructed, explicit scoring information is converted into implicit feedback information, and a user-film implicit feedback matrix A is constructed; 2, conducting user clustering, wherein film label information is utilized, user feature vectors are obtained by training an LDA topic model, and user clustering is achieved through a spectral clustering algorithm; 3, determining a local recommending model and training a global recommending model; 4, conducting model weight fusion recommending; 5, proving the effectiveness of the models through a leave-one-out method.

The embodiment of the invention provides an application recommending method and device. On one hand, through obtaining user behavior feature data of applications, the applications to be recommended are obtained from the candidate applications, and then according to the user behavior feature data of the applications, the applications to be recommended are sorted so that a sorting result can be obtained and output. In this way, according to the technical scheme, the pertinence and effectiveness of application recommending can be improved.

The embodiment of the invention discloses a method, a device and a system for recommending search terms. The method provided by the embodiment of the invention comprises the following steps of acquiring user behavior logs of a plurality of users, user portrayal information and preset user group characteristics of a plurality of user groups; calculating group heat of search terms according to the user behavior logs, the user portrayal information and the user group characteristics; calculating group distinction degrees of the search terms according to the user behavior logs, the user portrayal information and the user group characteristics; calculating the quality of the search terms according to the user behavior logs; recommending the search terms to the users according to the group heat of the search terms, the group distinction degrees and the quality of the search terms. According to the scheme, the individual recommendation for different user groups can be realized, the timely dynamic updating capacity is achieved, the accuracy of recommendation is increased and the recommendation effect is improved.

The present invention discloses a video recommendation method and a device, and belongs to the technical field of network video playing technology. The method comprises the steps of acquiring video data with a video type tag; acquiring the interest feature of a user; matching the similarity for the interest feature of the user with the video type tag of the video data; and pushing a video data list with the matching similarity thereof reaching a set value to the user. According to the technical scheme of the invention, the video data with the video type tag for representing the video type and the interest feature of the user are acquired. After that, the video type tag of the video data and the interest feature of the user are subjected to similarity matching. Finally, the video data list with the matching similarity thereof reaching the set value is pushed to the user. In this way, the video personalized recommendation is realized, so that the user can efficiently find a interested video. The user experience is improved, and the viscosity of the user to a video website is enhanced. Meanwhile, the brand value of the website is improved.

The invention discloses a user dynamic preference oriented commodity sequence personalized recommendation method. The method comprises the steps of extracting a commodity sequence under the same usersimilarity score to construct a commodity score vector; extracting a user sequence of similar scores of the same commodity to obtain a user score vector; combining user personal information, commoditybasic attribute information, user and article comments and commodity pictures; achieving feature extraction of a user and a commodity based on multi-task learning, taking a user feature vector and afeature vector of a historical commodity sequence of the user feature vector as input, achieving generation of the commodity sequence by training a coder-decoder, and accurately learning recommendation of the optimal commodity sequence in combination with a search strategy. According to the invention, based on the multi-modal user-commodity data, the user features and the commodity features are highly extracted and fused, personalized recommendation of the commodity sequence for user preferences is realized, and the user experience is improved.

The embodiment of the invention provides a service object recommendation method and device and electronic equipment. The method includes: acquiring live-streaming room attribute information; determining a target service object according to the live-streaming room attribute information; and displaying the target service object in a live-streaming room. By adopting the embodiment of the invention, an effect of sending the service object, which meets needs of a user of the terminal equipment, to the user of the terminal equipment in a personalized and pertinent manner is realized, and a recommendation effect is improved; and sending of invalid information is reduced, and network resources are saved.

The invention discloses a cross-border e-commerce platform having high concurrency extensible distributed type architecture. The cross-border e-commerce platform comprises a NODE front end, a NODE background, an interface layer, a service layer, a data access layer, and a data storage layer. A NODE rear end is used to provide a route request, a GET request, or an AJAX request for the NODE front end, and the interface layer is invoked to receive background request parameters, which are converted, and the service layer is invoked, and then the service layer is used to invoke the data access layer, which is used to invoke data from the data storage layer. The service layer comprises a member module, a commodity module, a logistics module, an order module, and a payment module. RPC remote invoking is adopted among the various modules of the service layer.

The invention relates to a parking recommendation method and device. The method comprises the following steps: receiving a parking request, wherein the parking request includes a vehicle identifier; obtaining user image information corresponding to the vehicle identifier; obtaining state information of a target parking lot; determining a parking space matching the vehicle identifier according to the state information and the user image information; and sending the information of the matching parking space to a terminal. By using the method and the device, users can be guided to park vehicles,personalized recommendation of parking spaces is realized, and the parking efficiency of users is improved.

The invention discloses a recommendation method of the semi-supervised learning based on a graph consistency model. The recommendation method comprises the following steps: S1 obtaining a server data set, constructing the graph consistency model for the data set, and establishing data points and sides; S2 measuring the similarity of two data points through a mapping function, and giving a weighted value to the side which exists between the data points; S3 establishing and solving an objective function, and sorting solutions of the objective function; S4 performing convergence proof on the sorted solutions of the objective function, so as to obtain a recommendation list, and sending the recommendation list to a user terminal. According to the recommendation method, personalized recommendation of user behavior information and article content information can be realized.

The invention discloses an image recommendation method merging visual features and user ratings. The method includes the first step of crawling a data set and extracting item images from the data setand a user's scoring matrix of the corresponding item images; the second step of utilizing a convolutional neural network (CNN) to extract image visual features of all the collected item images, to obtain a visual feature matrix; the third step of establishing a prediction preference model, and using an element-based alternating least squares method to update the prediction preference model; the fourth step of obtaining the user's preference values of all the item images from the final prediction preference model, sorting the preference values in descending order, selecting item images corresponding to top preference values, and recommending the item images to the user. The image recommendation method merges the visual features and the user ratings so that recommendation accuracy can be improved and personalized recommendations can be achieved.

The invention provides a method and an apparatus for recommending application information based on installed applications. The method comprises the steps of determining the similarity between applications installed by a user and different information in an information flow library; obtaining matching degrees of the user to the different information by utilizing the obtained similarity; and selecting a certain quantity of information as candidate information according to a sequence of the matching degrees from high to low, and recommending the information to the user according to the sequence of the matching degrees from high to low.

The embodiment of the invention provides a method and device for application recommendation and a server. The method comprises the steps of obtaining attribute labels corresponding to target users; according to the attribute labels and a preference value of each application by a user group having the attribute label, obtaining preference values of the target users for each application; and based on the preference values of the target users for each application, recommending applications for the target users. According to the method, under the conditions of no supervision, the preference of the user group under the different attribute labels is automatically identified according to user behavior data, personalized recommendation for the users is performed according to the attribute labels which the users have, the mode greatly reduces human cost, and the recommending accuracy is improved through objective analysis of the user behavior data.

The embodiment of the invention discloses an information recommendation method, device and equipment and a medium, and relates to the technical field of information. The method comprises the followingsteps of determining at least one historical user similar to a target user as a reference user according to the user characteristics; taking the target class article associated with the historical behavior of the reference user as a candidate article; determining the weight of the candidate article according to the historical behavior and historical behavior weight of the reference user on the candidate article and the similarity between the reference user and the target user; and according to the weight of the candidate item, recommending a target item to a target user. According to the information recommendation method, device and equipment and the medium provided by the embodiment of the invention, the personalized recommendation of the credit card information to the user is realized,so that the personalized demand of the user on the credit card is met, and the information recommendation accuracy is further improved.

The invention provides a commodity recommendation method, a device, a computer device and a storage medium. The method comprises the following steps: crawling user behavior data of all users on a preset social platform by using a web crawler technology; obtaining basic information and behavior characteristic information of all users according to the user behavior data; calculating a similar user group of the target user based on basic information and behavioral characteristic information of all the users if an instruction to recommend a commodity to the target user is received; acquiring sub-data of the similar user groups for preset commodities; calculating a recommendation score for each preset commodity for the similar user group based on the rating data; a merchandise being recommendedto the target user according to the recommendation score. The embodiment of the invention utilizes the user behavior data of the social platform to calculate the similar user groups of the users, andbased on the collaborative filtering thought, utilizes the evaluation behavior of the similar user groups to recommend the goods to the users, realizes the personalized recommendation, and improves the accuracy rate of the goods recommendation.

The invention discloses an information recommending method, comprising: obtaining relevant information of users wherein the relevant information comprises the attribute information and / or purchase information; quantifying the attribute information and / or purchase information; based on the quantified information, calculating the similarity among users; according to the similarity, conducting classifications; marking the users in the same classification; and recommending commodity information to users marked in the same classification. The invention also discloses an information recommending apparatus. With the invention, it is possible to mark users and to accurately recommend commodity information to the users. It is also possible to realize customized recommendations.