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166results about How to "Solve the sparsity problem" patented technology
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Collaborative filtering recommendation algorithm based on article sorting and user sorting
ActiveCN102609523ASimple methodImprove scalabilitySpecial data processing applicationsExtensibilityCollaborative filtering
The invention relates to a collaborative filtering algorithm, in particular to a collaborative filtering recommendation algorithm based on article sorting and user sorting, which is characterized by including steps: A, article clustering and sorting; B, user clustering and sorting; C, integration of article clustering and user clustering; and D, sorting recommendation. Compared with the prior art, collaborative filtering recommendation algorithm has the advantages that the data clustering is completed by means of modified KMEANS algorithm, the method is simple, and extensibility is improved while the problems of sparsity and cold boot are solved.
Owner:上海视畅信息科技有限公司
Cluster-based collaborative filtering commodity recommendation method and system
InactiveCN103412948ASolve the sparsity problemImprove accuracySpecial data processing applicationsMarketingRating matrixCluster result
The invention belongs to the technical field of data mining and particularly relates to a cluster-based collaborative filtering commodity recommendation method and system. The method comprises obtaining scoring information of users on commodities and type tag information of the commodities through APIs (application program interfaces) of shopping websites; clustering the users through the types of commodities purchased by the users; according to the clustering results, assigning scoring assessment values for default scores in a user-commodity scoring matrix through a scoring assessment formula; calculating the similarity among the commodities in the matrix, predictively scoring the commodities not purchased by the users and recommending the top N commodities with the highest predictive scores to target users. Compared with the prior art, the cluster-based collaborative filtering commodity recommendation method and system has the advantages of solving the problem of data sparsity, reducing the problem of inconsistent scoring scales of various users, enabling the scoring similarity of users of the same type to be the highest and improving the accuracy of default assignment.
Owner:BEIJING JIAOTONG UNIV
Clustering collaborative filtering recommendation system based on singular value decomposition algorithm
ActiveCN103093376AImprove recommendation accuracyOptimize recommendation resultsMarketingSingular value decompositionImaging processing
The invention provides a clustering collaborative filtering recommendation technology based on a singular value decomposition algorithm. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm comprises firstly classifying users by using user attributive character values provided by the clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm, and reducing dimension of a user-commodity grade matrix; improving a singular value decomposition (SVD) algorithm which is frequently used in image processing and natural language processing, and using the improved SVD algorithm in a recommendation system; decomposing a grade matrix in a cluster where users are located, and aggregating the decomposed grade matrix so as to fill predicted scores of non-grade items in the grade matrix, calculating similarity of the users in the same cluster by using the filled grade matrix, calculating final predicted scores of a commodity by applying collaborative filtering technologies based on the users and widely applied in the recommendation system, and carrying out final recommendation. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm has the advantages of being capable of improving recommendation efficiency of the recommendation system, solving the problems such as data sparsity of the recommendation system, and meanwhile being capable of improving accuracy rate of recommendation of the recommendation system.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Film individuation recommendation method based on user real-time interest vectors
ActiveCN104063481AReal interest vectorReasonable modeling granularitySpecial data processing applicationsFeature vectorPersonalization
The invention discloses a film individuation recommendation method for combining film content and user real-time scoring information. The problem that a traditional recommended algorithm cannot reflect user interest change and data sparsity in time is mainly solved. In order to solve the data sparsity problem, the user interest vectors are introduced in the film individuation recommendation method. Starting from the film feature vectors, the obtained user interest feature vectors are processed in an iterative mode by the aid of a user scoring matrix, a user similar matrix is established according to the obtained user feature vectors, and finally recommendation can be achieved according to a traditional collaborative filtering scoring predicator formula. According to the user interest change condition, time factors are further integrated in the establishing process of the user interest vectors, the scoring behavior weight is bigger when scoring time more approaches the current time, and the user interest can be more represented.
Owner:SHANDONG UNIV
Personalized research direction recommending system and method based on themes
ActiveCN103425799AUnobscuredOvercome the defect of increasingly narrow field of viewSpecial data processing applicationsPersonalizationField of view
The invention discloses a personalized research direction recommending system and method based on themes. Paper topics read by users and preference of the users for related paper topics can be obtained through the recommending system according to all the papers read by the users and according to the themes of the papers obtained when training is conducted through a theme model training module, therefore, the recommending system can recommend a new research direction for the users to widen the vision of the users. The innovation key of the personalized research direction recommending system and method based on the themes is to construct a three- layer graph model according to the relationship between the users and the papers and the relationship between the papers and the themes, to calculate preference values of the users for the themes according to the three-layer graph model, to obtain a user-theme preference weight matrix, and to calculate similar user set between the users and other users based on the weight matrix. The preference degree of the themes which are not touched by the users is predicted according to the similarity value of the similar users in the similar user set and according to the preference values of the similar users for the themes, and the research direction, namely, the research theme, is recommended for the users according to the prediction result.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Short text characteristic expanding method based on semantic atlas
ActiveCN104391942AImprove classification performanceSolve the sparsity problemSemantic analysisSpecial data processing applicationsGraph spectraData set
The invention discloses a short text characteristic expanding method based on a semantic atlas. The method includes the steps: performing subject modeling by the aid of a training data set of a short text, and extracting subject term distribution; reordering the subject term distribution; building a candidate keyword dictionary and a subject-keyword semantic atlas; calculating comprehensive similarity degree evaluation of candidate keywords and seed keywords based on a link analysis method, and selecting the most similar candidate keywords to finish expanding the short text. Compared with a short text characteristic representation method based on a language model, the method is simple to operate and high in execution efficiency, and semantic correlation information between the keywords is sufficiently used. Compared with a traditional short text characteristic representation method based on a word bag model, the problems of data sparseness and semantic sensitivity are effectively relieved, and the method is independent of external large-scale auxiliary training corpus or a search engine.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
Collaborative filtering method based on socialized label
InactiveCN101853470ASolve the sparsity problemSolve the noiseCommerceSpecial data processing applicationsPersonalizationAlgorithm
The invention discloses a collaborative filtering method based on socialized labels, which comprises the following steps that: (1) first, a tripartite graph is used to build models for three different node, i.e. a user, an article and the socialized label, and random walk algorithm is applied to each user to recommend top-N personalized articles; (2) in order to solve the problem of the sparsity of socialized labels (i.e. articles are always labeled by only a small number of labels) and noise brought by the subjective factors of the user, the invention provides a method for expanding the labeling of the articles with a lasso logistic regression model, i.e. labels related to the semantics are increased for each article, and the labels with noise are removed; and (3) the weight of the labels in the recommendation process is regulated. The collaborative filtering method based on socialized labels organically integrates the semantic information of the socialized labels to the description of the articles, uses the lasso logistic regression model to expand the labels, solves the sparsity and noise problems of the socialized labels so as to greatly improve the precision and the performance of a personalized recommendation system.
Owner:ZHEJIANG UNIV
Collaborative filtering method for personalized recommendation fusion content and behavior
ActiveCN108205682ADig deep into long-term preferencesImprove calculation accuracyCharacter and pattern recognitionBuying/selling/leasing transactionsPersonalizationNear neighbor
The invention relates to a collaborative filtering method for personalized recommendation fusion content and behavior. The method comprises the following steps of, (1) characteristic input, includinga project-attribute matrix representing a project content and a user behavior matrix representing user behaviors; (2) content-based project clustering for calculating the similarity of projects and clustering the projects; (3) score prediction and feature filling including carrying out score prediction on the non-scoring projects, and filling a user-project scoring matrix; (4) behavior-based userclustering including clustering users according to a project clustering result and a user-project scoring matrix; (5) score predication and project recommendation including determining the clusteringcluster where the target users are located, finding a nearest neighbor user set, performing score prediction on the non-scoring projects of the target users, and finally recommending the first N projects with the highest prediction scores to the target users. Compared with the prior art, the collaborative filtering method effectively solves the problems of data sparsity and cold start, and ensureshigh recommendation efficiency.
Owner:TONGJI UNIV
Knowledge map representation learning method which combines entity and relationship description
ActiveCN108197290AGood practicalitySolve the sparsity problemSpecial data processing applicationsLearning methodsKnowledge level
The invention discloses a knowledge map representation learning method which combines an entity and a relationship description. Text description information of the entity and the relationship is takeninto consideration, two information sources of structured information and the text description information of a triple are well integrated, based on combined extracts of an end-to-end model of neuralnetworks conducted on the entity and the relationship, a balance factor is set to balance the structured information and the text description information, and different score functions are defined according to different prediction objects; a loss function is used for correlating an entity vector and a relationship vector, the loss function is optimized, and when an optimization goal is reached, the vectors of each entity and relationship in the knowledge map and the text description information can be learned. The method solves the sparsity and imbalance of entity and relationship in a knowledge base, accurately and effectively represents the interconnection between the entity and relationship, applies the interconnection to the large-scale knowledge map, and has good practicality.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Webpage training method and system and webpage prediction method and system
ActiveCN103914478ASolve the sparsity problemSpecial data processing applicationsWeb siteFeature vector
The invention relates to a webpage training method and system and a webpage prediction method and system. The webpage training method comprises obtaining a prior probability table of classified keywords according to existing data which are associated with the classified keywords; preprocessing a webpage to be trained to obtain a webpage text to be trained; extracting features in the webpage text to be trained according to the prior probability table to obtain an association relation feature vector representation F1 between the webpage to be trained and a specified category; performing model training on the association relation feature vector representation F1 to obtain a classification result of the webpage to be trained. According to the webpage training method and system and the webpage prediction method and system, category systems which are strong in heterogeneity can be simultaneously processed, the large category systems can be processed through less training data, and the problem of data sparseness is largely solved due to the fact that the browsing and search behavior of a user on the whole Internet not just a website is collected.
Owner:ALIBABA GRP HLDG LTD
Bayesian model-based commodity code classification method and system
ActiveCN107704892ASolve the sparsity problemSolving Encoding Classification ProblemsCharacter and pattern recognitionNatural language data processingClassification methodsConditional probability
The invention provides a Bayesian model-based commodity code classification method and system. Aiming at the problems of sparsity and context loss caused by short text features of commodity name information, the method and system adopt a synonym extension technology for word-segmented sparse words, and increase the word synonym degrees through an external search engine technology; and then, a Bayesian model is adopted to calculating condition probabilities between word sequences and code categories of commodity names so as to obtain a Bayesian code classification mode, and a trained model is used for predicting commodity code categories of input commodity names.
Owner:NINGBO AISINO +1
Collaborative filtering recommendation algorithm based on graph convolution attention mechanism
PendingCN112905900AEmbedded validImprove accuracyDigital data information retrievalNeural architecturesData setAlgorithm
The invention discloses a collaborative filtering recommendation algorithm based on a graph convolution attention mechanism. The method comprises the steps of firstly collecting and processing data and dividing a data set, secondly constructing a GACF model, and finally training the model and recommending by predicting an association score between a user and an item. According to the graph convolution attention mechanism collaborative filtering model provided by the invention, firstly, user-project interaction information is mapped to a vector space by using a graph embedding technology, then, the embedding expression of a user-project interaction graph is learned through a graph convolution network, and then, different weights are allocated to neighbor nodes by using an attention mechanism. By aggregating the feature information of the neighbor nodes, the weight between the neighbor nodes can only depend on the feature expression between the nodes, so that the generalization ability of the model is improved, and finally, a plurality of embedded vectors learned by a graph convolution layer are weighted and aggregated to obtain the association score between the user and the project.
Owner:LIAONING TECHNICAL UNIVERSITY
A method for recommending knowledge map based on location service domain
ActiveCN109255033ALayeredSolve the sparsity problemCharacter and pattern recognitionText database clustering/classificationCluster algorithmService domain
The invention discloses a recommending method of a knowledge map based on the location service field, which includes: extracting a location entity and obtaining an entity set as a seed set of the knowledge map, corresponding the seed set with the entities in the knowledge map, forming an entity correspondence table, in a knowledge map triple in that knowledge map, using Word2Vec model to embed vocabulary into n-dimensional space, generating corresponding vectors, obtaining a position or domain entity vector set E and a relation vector set R, translating the entity vector set E and the relationvector set R by using a TransE algorithm, and obtaining a triple vector set capable of quickly calculating semantic similarity between entities; according to the location or domain entity vector setE, calculating respectively the semantic similarity simA, B (A, B) between the searching locations or domains to generate the semantic similarity matrix of the tourism location, using Semantic Similarity Matrix for Top-K Recommendation List, clustering the recommendation list according to machine learning clustering algorithm, and recommending the clustering result to the user. The method has highprecision and can solve the problems of cold start and sparsity.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Method and device for recommending articles to user by website
InactiveCN102722838AImprove experienceIncrease viscositySpecial data processing applicationsMarketingWeb siteRecommendation quality
The invention provides a method and a device for recommending articles to a user by a website. The method comprises the following steps of: receiving the selection of the user for a first article; inquiring a second article database related to the first article and the historical records of the user according to registration materials of the user on the website, and obtaining a group to which the user belongs according to the historical records of the user; recommending a second article related to the first article according to the second article database, the historical records of the user and the group to which the user belongs; receiving the evaluation of the user to the first article; and recoding the evaluation of the user to the first article to the historical records of the user. According to the method, an interest point can be positioned rapidly, user experience can be improved remarkably, the user viscosity can be enhanced, the recommendation accuracy can be improved, and simultaneously the articles can be recommended to a new user according to the group, the problem of data sparsity is solved effectively, and moreover, the recommendation quality can be improved according to the feedback of a recommendation result of the user.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Personalized POI recommendation method based on multi-influence embedment
ActiveCN107133262AQuality improvementSolve the sparsity problemSpecial data processing applicationsTime frameData science
The invention discloses a personalized POI recommendation method based on multi-influence embedment. By conducting associative embedded learning on seven bipartite graphs (a user-user graph, a user-time frame graph, a POI-time frame graph, a POI-region hierarchy graph, a POI-category hierarchy graph, a user-gender graph and a user-POI graph) and a check-in sequence, influences on the aspects of social contact, time, geography, semantics, user gender, user preference and sequence are integrated; meanwhile, certain expansibility is achieved, influences on other aspects can be conveniently integrated, so that the problems of data sparsity and cold start are effectively solved, and high-quality POI recommendation is provided for users.
Owner:ZHEJIANG UNIV
Short text topic model mining method based on word network to extend characteristics
ActiveCN106055604ASolve the sparsity problemAddress underperformanceSpecial data processing applicationsText database clustering/classificationTopic miningText corpus
A short text topic model mining method based on a word network to extend characteristics comprises a weighted word network construction step, a short text characteristics extending step, and a topic mining step. The weighted word network construction step comprises preprocessing a text, performing Chinese words segmentation on the text in a short text corpus, and deleting stop words; establishing a weighted word network from a document after the Chinese words segmentation is performed, wherein nodes in the weighted word network are words, each edge between the nodes is cooccurrence relation of two words in the same document, and the weight of the edge is the cooccurrence time of the two words in the whole corpus; and ending. The short text characteristics extending step comprises using the word nodes included by each short text after the Chinese words segmentation is performed as a community of the established weighted word network. According to the short text characteristics sparsity solution method based on word network community module degree, the problem that the effect of applying an LDA topic model to the short text is poor is solved. Accuracy of a short text topic model is increased.
Owner:NANJING UNIV
Recommending method for mining depth user similarity based on interactive sequence data
ActiveCN109522474ASolve the sparsity problemImprove recommendation accuracyDigital data information retrievalBuying/selling/leasing transactionsPersonalizationRecommendation model
The invention provides a recommending method for mining depth user similarity based on interactive sequence data. The recommending method comprises the following steps: pre-processing the original recorded data of the interaction between the user and the article to obtain interactive sequence data of the user and the article, and generating the user according to the interactive sequence data; themethod comprises the steps of: mining the depth user similarity based on the interactive sequence data; mining the depth user similarity; mining the depth user similarity based on the interactive sequence data. Item Interaction Matrix and Users-Gram matrix; In accordance with that user -Item interaction matrix and the user-gram matrix constructs a recommendation model and trains the recommendationmodel; Based on the trained recommendation model, each user's preference for all items is calculated, and all items are sorted from high to low according to the preference value. The items with the highest preference value among the remaining items are recommended to the corresponding users as the result of personalized recommendation by removing the items that the user has interacted with. The invention solves the problem of data sparseness and improves the recommendation accuracy by applying the preference of the user to the article and the similarity among the users to the recommendation method.
Owner:SHANGHAI JIAO TONG UNIV
GCN-based text classification method
ActiveCN111274405ASolve the sparsity problemAccurate resultNatural language data processingNeural architecturesClassification resultTest set
The invention discloses a GCN-based text classification method. The method comprises the steps of obtaining a text classification corpus set, wherein the corpus set comprises a plurality of samples, and each sample comprises a title and a chapter; preprocessing the corpus set, and dividing the preprocessed corpus set into a training set, a verification set and a test set; processing the chapters through space, and extracting a graph relationship between words; embedding each word into a low-dimensional real value vector space of the matrix according to the graph relationship; constructing a bidirectional LSTM according to the vector representation of the word, and obtaining sentence representation; reconstructing sentence representation based on a self-attention mechanism, inputting the sentence representation into a GCN neural network, and training a semantic classification model; inputting the text word vector of the verification set into the model, and recording and storing model parameters when the effect on the verification set is optimal; and testing the test set based on the optimal model obtained by the verification set to obtain a classification result. According to the method, a more accurate class result is finally obtained by applying LSTM and GCN in combination with an attention mechanism.
Owner:BEIJING UNIV OF TECH
System of mobile-phone application recommendation of multi-label classification and method thereof
ActiveCN107609063AReliable serviceAddress initial data sparsitySpecial data processing applicationsBusiness logicMulti-label classification
The invention belongs to the technical field of big data and data mining and recommendation systems, and particularly relates to a system of mobile-phone application recommendation of multi-label classification and a method thereof. The system of the invention includes five modules of a data acquisition module, a data processing module, a data storage module, a service logic module and a display module. A multi-label classification algorithm based on a random walk algorithm is provided in the invention, and multi-label data are mapped into a multi-label random walk graph. A multi-label randomwalk graph series is established when unclassified data are input; and then each node in the graph series is randomly walked to obtain probability distribution of traversing each vertex, and the probability distribution of the vertex is transformed into probability distribution of each label. According to the system and the method, a problem of user interest diversity recommendation and a problemof recommendation operation complexity increasing brought by constant user interest changes are solved, recommendation technology which is more flexible than traditional recommendation technology is obtained, and recommendation quality is improved.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Movie recommendation method based on feedback of users' various behaviors
ActiveCN104462383ASettle the lossSolve the sparsity problemSpecial data processing applicationsFeature selectionData mining
The invention discloses a movie recommendation method based on feedback of users' various behaviors. The method includes the steps of s1, clustering movies, to be specific, subjecting movie information to feature selection to obtain keyword description of each movie; s2, calculating user similarity, to be specific, clustering users into a plurality of user sets by means of content clustering-behaviors based on the fuzzy theory, with each user having different membership degrees in the different user sets, performing modeling with movie description information and feedback data of users' various behaviors, calculating membership degrees of each user in the user sets, and calculating user similarity according to the membership degrees of the users in the different user sets; s3, generating recommendations, to be specific, generating different movie recommendation lists for the users according to the acquired user similarity. The method helps solve the problem of data sparsity, solve the problem of information loss occurring in traditional 'implicit-explicit' conversion and improve recommendation precision.
Owner:SHANDONG UNIV OF SCI & TECH
A method for completing continuous missing data of dam deformation monitor
ActiveCN109101638AReduce redundancySolve the sparsity problemData miningNon-redundant fault processingMissing dataAlgorithm
The invention discloses a method for completing continuous missing data of dam deformation monitoring, which solves the problem of completing continuous missing data of dam deformation monitoring. Firstly, the continuity loss of deformation monitoring data is pretreated. Secondly, from the angles of global space, global time, local space, local time and semantics, the spatio-temporal similarity and functional similarity of deformation monitoring points are calculated respectively, and the missing data in deformation monitoring data are interpolated globally and locally in space, time and semantics. Finally, a depth neural network model is constructed to complete the continuous missing data in dam deformation monitoring. The preliminary results of the missing data are used as input, and thenonlinear fusion is realized by using the depth neural network representation ability.
Owner:HOHAI UNIV
Order-preserving submatrix (OPSM) and frequent sequence mining based emotion classification method for e-commerce comments
ActiveCN107357837ASmall scaleReduce time complexitySemantic analysisSpecial data processing applicationsCluster algorithmClassification methods
The invention discloses an order-preserving submatrix (OPSM) and frequent sequence mining based emotion classification method for e-commerce comments. The method comprises the following steps: (1) performing pretreatment and Chinese word segmentation on the e-commerce comments; calculating to obtain a TF-IDF weight vector of synonyms; and then mining a local mode in the weight vector through a biclustering algorithm based on OPSM; (3) mining classification frequent phrase characteristics through an improved PrefixSpan algorithm, and meanwhile, improving the capacity for distinguishing emotion tendency by the frequent phrases through limitation such as word intervals; and (4) converting the characteristics mined in steps (2) and (3) into a 0 / 1 vector to be used as an input of a classifier, and thus obtaining the emotion classification result of the e-commerce comments. With the adoption of the method, the emotion classification characteristics of the e-commerce comments can be accurately mined, so that potential customers can know the goods evaluation information before buying, and meanwhile, the businessman can fully know the suggestions of the customers and accordingly improve the service quality.
Owner:山东云从软件科技有限公司
Tensor decomposition and weighted HITS-based time perception personalized POI recommendation method
ActiveCN106960044ASolve the sparsity problemQuality improvementSpecial data processing applicationsTensor decompositionData mining
The invention relates to a tensor decomposition and weighted HITS-based time perception personalized POI recommendation method. For the problem of data sparsity confronted in a conventional POI recommendation method, firstly a user preference is modeled by introducing cooperative tensor decomposition of additional information; secondly POIs are scored by integrating the user preference and the popularity of the POIs through weighted HITS; and finally a plurality of POIs ranked in front are provided for a user according to the scoring of the POIs to serve as recommended POIs. By integrating the cooperative tensor decomposition and the weighted HITS and considering three factors including the user preference, time and local characteristics, the problem of the data sparsity is solved and high-quality personalized POI recommendation is provided for the user.
Owner:ZHEJIANG HONGCHENG COMP SYST
Collaborative filtering-based content recommendation system and method
InactiveCN106610970ASolve the sparsity problem and concept drift problemImprove matchSpecial data processing applicationsA-weightingMechanism based
The invention discloses a collaborative filtering-based content recommendation system and method. The system comprises a data preprocessing module, an algorithm mixing module, and a result generation module, wherein the algorithm mixing module further comprises an algorithm selection unit, a weighting similarity-based collaborative recommendation algorithm unit, a balance score prediction mechanism-based collaborative recommendation algorithm unit, a score filling-based mixed recommendation algorithm unit, and a collaborative recommendation algorithm unit using score time characteristics; and the algorithm mixing module inputs preprocessed data to the weighting similarity-based collaborative recommendation algorithm unit, the balance score prediction mechanism-based collaborative recommendation algorithm unit, the score filling-based mixed recommendation algorithm unit, and the collaborative recommendation algorithm unit using the score time characteristics, and outputs an algorithm result to the result generation module. According to the system and the method, the sparsity problem and the concept drift problem in the recommendation system are better solved.
Owner:上海文广互动电视有限公司
Continuous interest point recommendation method based on check-in time interval mode
ActiveCN109492166ASolve the sparsity problemAccurate and efficient continuous POI recommendation serviceDigital data information retrievalSpecial data processing applicationsParameter learningStatistical model
The invention relates to a continuous interest point recommendation method based on check-in time interval mode, belonging to the recommendation system field. According to the check-in data of each user, synthesizing personalized preference, geographic distance preference and check-in time interval preference to form the user's comprehensive preference for the next interest point to be visited, and adopting the third-order tensor model to model the continuous check-in behavior; a probabilistic model is constructed to learn the comprehensive preference degree of the user to the points of interest by taking the check-in interval preference as a potential variable. In the parameter learning phase, the expectation maximization algorithm is designed to optimize the parameters of the probabilitymodel, and finally the task of recommending the next point of interest for users to visit is realized. The tensor / matrix factorization algorithm is used to compensate the missing information in the tensor and matrix. Compared with the prior art, the method of the invention effectively solves the problem that the method of the invention effectively solves the sparsity problem of the user-point ofinterest sign-in matrix, and provides the user with an accurate and efficient continuous interest point recommendation service.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
Personalized recommendation method based on dynamic neighboring point spectral clustering
ActiveCN107885778AEffective clusteringSolve the sparsity problemRelational databasesBuying/selling/leasing transactionsPersonalizationSpectral clustering algorithm
The invention relates to a personalized recommendation method based on dynamic neighboring point spectral clustering. A user-store bipartite network is set up according to user sign-in information; the user-store bipartite network is projected to a user-user one-aside network and a store-store one-aside network, and a node2vec algorithm is used for projecting the two weighted one-aside networks totwo different vector spaces; the spectral clustering algorithm based on dynamic neighboring points is called to cluster user vectors and store vectors obtained above to obtain multiple user clustersand multiple store clusters; the sign-in information existing between single users is converted into a cluster network among the user clusters and the store clusters; a K-means algorithm is used for dividing the one-dimension vector into two classes, the store clusters in the class with the larger sign-in average number are recommended to the user clusters; personalized recommendation is conductedaccording to each user cluster and the recommended store clusters. The accuracy of the recommendation method is improved effectively.
Owner:ZHEJIANG UNIV OF TECH
Electronic commerce recommending method based on support vector machine (SVM)
InactiveCN103886486ASolve the sparsity problemAddress effectivenessMarketingFeature vectorSvm classifier
The invention discloses an electronic commerce recommending method based on a support vector machine (SVM). The method includes the steps that a user evaluation predication model based on the SVM is established, textual characteristics including semantics are extracted based on machine learning, evaluations are expressed as multi-dimensional characteristic vectors, the evaluations are classified, an SVM classifier divides the commodity evaluations into useful and useless categories, and the evaluations are automatically recognized; according to useful scores of the evaluations in the classifier, a user item matrix is filled by the adoption of a method of scoring predication through training samples according to the scores; according to importance of all items, corresponding vectors of a kernel function are endowed with corresponding weights, and meanwhile the weights of the corresponding vectors are corrected according to user process behaviors so that the purposes of improving predication precision and achieving an ideal recommending effect can be achieved. Statistics, machine learning, intelligent mode recognition and classification and other technologies are used for analyzing customer electronic commerce access behaviors and the commodity evaluations, commodities which a customer is interested in are predicated through the mode, a recommending result is generated and recommended to the customer, and the customer is helped to rapidly and accurately find out commodities really needed in time.
Owner:JISHOU UNIVERSITY
An intelligent recommendation system based on big data
InactiveCN109670116AReal-timeAccurate recommendationDigital data information retrievalBuying/selling/leasing transactionsClient-sideData science
The invention discloses an intelligent recommendation system based on big data, and belongs to the data processing technology field. The system comprises a client used for collecting the log information and the real-time operation attributes of a user, writing into a server and displaying the recommendation result information fed back by the server; the server used for collecting the user log information and the real-time operation attributes, adopting a deep learning technology for data analysis and establishing a user interest model; and a cloud big database which is used for screening out the products to be recommended in real time by utilizing a recommendation algorithm according to the user interest model and returning the products to be recommended to the client for display through the server, wherein the client is connected with the server, and the server is connected with the cloud big database. According to the present invention, the problems that a traditional recommendationsystem is inaccurate in recommendation information, does not have real-time performance and is greatly limited by the environment, are solved.
Owner:内江亿橙网络科技有限公司
LBSN supernetwork link prediction method based on time-space relationship
ActiveCN107784124ASolve the sparsity problemImprove noise immunityData processing applicationsSpecial data processing applicationsData sourceModel parameters
The invention relates to an LBSN supernetwork link prediction method based on time-space relationship and belongs to the field of data mining. The method comprises the following steps: S1, acquiring adata source; S2, constructing a supernetwork model; S3, defining and quantizing a supernetwork edge weight value; and S4, based on the model, firstly constructing multiple types of weighting hyperedge structures, mining different semantic relations among users by virtue of different structures, finally training model parameters by virtue of a gradient descent method, and then predicting link relations in network. The method provided by the invention has the advantages that multiple incidence relations among nodes can be effectively mined, the sparsity problem in the network can be solved, anti-noise property and stability of the model also can be improved, and predictive accuracy of the model is also greatly improved.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Friend recommendation system and method based on community detection
InactiveCN108399189AReflect the probability of check-in timeEmbody similaritySpecial data processing applicationsGeolocationData acquisition
Owner:CHONGQING UNIV OF POSTS & TELECOMM
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