47 results about "Rating matrix" patented technology

The invention discloses a position recommendation method based on a collaborative filtering algorithm, and relates to the technical field of data analysis. The method comprises the steps of 1, collecting and preprocessing data, 2, constructing a user-position scoring matrix: constructing the user-position scoring matrix according to historical behavior data of users, the rows of the user-position scoring matrix representing users and the columns of the user-position scoring matrix representing positions, elements in the matrix representing association scores of the users to the positions, and elements in the matrix representing association scores of the users to the positions; the method comprises the following steps of 1, selecting a total score of the positions, 2, performing collaborative filtering on the job seekers and collaborative filtering on the positions on the basis of a collaborative filtering algorithm, 4, sequencing the positions according to the total score of the positions, generating a position list and recommending the position list to a target user, and 5, collecting feedback of the user on a position recommendation result for a subsequent optimization recommendation process.

The invention discloses a micro spring washer manufacturing process optimization method using a collaborative filtering algorithm, and the method comprises the following steps: S1, collecting and preprocessing historical manufacturing data, and constructing a tensor structure comprising manufacturing batches, process parameters and product quality indexes; s2, converting the tensor structure into a scoring matrix, and predicting an initial process parameter combination of a target batch by adopting a collaborative filtering algorithm; s3, constructing a weighted graph, introducing a label propagation network to complement scores, and determining a recommendation parameter combination; s4, constructing a multi-objective optimization model with size consistency, residual stress balance and unit energy consumption as objectives based on the recommended combination; s5, solving by adopting an improved cat swarm optimization algorithm, and outputting an optimal solution; and S6, applying the optimal solution to the manufacturing process, collecting quality result feedback, updating the scoring matrix and the graph model, and realizing a manufacturing optimization closed loop. According to the method, the tag propagation network and the improved cat swarm optimization algorithm are fused, and the manufacturing process of the miniature spring washer is optimized.

The invention discloses a product recommendation method based on personality features and FCM clustering optimization. The method comprises the steps that 1, a user and article set and a user big five personality feature score set are obtained; 2, clustering is carried out based on big five personality data of the user, and an optimal clustering center and an optimal membership matrix are obtained; 3, complementing the missing value of the user-article scoring matrix based on user personality feature clustering, so as to calculate the user similarity; and 4, calculating the preference similarity between the user and the target user according to the user-article scoring matrix, calculating the personality similarity between the user and the target user according to the personality characteristics of the user, and carrying out weighted operation to obtain the user similarity so as to calculate the predicted score of the user. According to the method, the cold start problem, the data sparsity problem and the interest drifting problem of user interests along with time change in a recommendation system can be effectively solved, so that the individuation, accuracy and reliability of product recommendation can be improved, and higher conversion rate and user stickiness are brought to a sales platform.

The invention discloses a collaborative filtering recommendation method based on user article scores fused with multi-dimensional evaluation indexes, and the method comprises the following steps: a score data preprocessing stage: inputting a score matrix, optimizing a loss function of BiasSVD by using a stochastic gradient descent method, obtaining parameters required by a BiasSVD score calculation formula, and calculating the score of the user article scores according to the parameters required by the BiasSVD score calculation formula; outputting a complete score data table with enhanced robustness; in the multi-dimensional evaluation index establishment stage, three key indexes are calculated, and the indexes are subjected to normalization calculation to form a multi-dimensional novelty evaluation framework; in the recommendation generation stage, according to the algorithm, a post-processing weighted sorting mode is combined with a multi-dimensional novelty index and user article score data, the user similarity is calculated through cosine similarity, scores of the users on the articles are predicted through a weighted summation method, and a comprehensive recommendation list is generated, so that the problem of data sparsity of a traditional collaborative filtering algorithm is effectively solved; the problems of recommendation result homogenization, insufficient novelty and long tail effect are solved.

The deep neural network recommendation system with prediction reliability comprises the following steps: obtaining and cleaning data information from a database to obtain required information; dividing a user-item rating matrix R into corresponding several independent and only containing 0-1 binary sub-matrices according to different rating values set by the system; using a double-tower model in a deep neural network to train the obtained sub-matrices in parallel to predict the probability value of the same blank area in each sub-matrix; normalizing the obtained probability value in each sub-matrix, and taking the maximum normalized probability value as the reliability probability; finding the rating represented by the corresponding sub-matrix according to the obtained reliability probability, and taking the rating as the predicted rating of the blank area; comparing the predicted rating with the reliability threshold value set by the system, filtering the predicted rating with a reliability probability lower than the threshold value, and retaining the rating with a higher reliability probability; sorting the filtered predicted rating set, pushing the first k items with higher predicted ratings to the target user, and forming a personalized recommendation list.

The invention discloses a data poisoning attack method based on a deep learning recommendation algorithm, and the method comprises the steps: precisely screening abnormal scoring users and interaction items thereof through a statistical method in combination with a non-consistency measurement NCM and P-value, and constructing a selection item set; secondly, injecting a forged user, endowing a selected item set and a target item set with the highest score, scoring a filling item set according to normal distribution, and forming a user-item score matrix R'with poisoning data; a double-layer iterative optimization framework is adopted, and an outer layer maximizes a target project hit rate through attack loss CWLoss, enhances loss SFALoss through spectral features and hits attack traces through regularization. And the inner layer uses the user-project scoring matrix R'of the poisoning data to update the model parameters based on the deep learning recommendation algorithm until the attack converges. According to the method, the recommendation result can be remarkably manipulated under the condition of only a small number of poisoning users, high concealment and high attack efficiency are both achieved, and an effective means is provided for evaluating and improving the robustness of a recommendation system.

This invention discloses a PNN-based multi-feature extended autoencoder recommendation algorithm in the field of personalized data recommendation research, comprising the following steps: extending the auxiliary feature information of items through the open knowledge graph DBpedia; representing the extended feature information as an LsiModel vector and using PNN for feature cross-fusion to mine the potential feature relationships between different features and embed them into low-dimensional feature vectors; merging rating information, attribute information, and extended feature information into a semi-autoencoder, extracting robust feature representations through the semi-autoencoder to help the rating matrix better reconstruct the output, comparing it with the original rating matrix, calculating the prediction accuracy, and achieving more accurate recommendations. This invention utilizes knowledge graphs to extend item feature information and effectively fuses feature relationships between different features, learning higher-level feature representations through a semi-autoencoder to achieve the goal of providing more accurate recommendations for users.

The present invention discloses a method for recommending oilfield cloud application components based on a coefficient of variation method, comprising the following steps: S1, collecting oilfield cloud application component log data; S2, data normalization processing; normalizing an oilfield application component evaluation matrix to eliminate dimensional differences between different indicators, so that the numerical value of each indicator is mapped to an interval between [0, 1], and obtaining a standardized evaluation matrix; S3, constructing a virtual rating matrix by using the coefficient of variation method; S4, establishing a recommendation model; establishing an oilfield cloud application component recommendation model by combining a user-based collaborative filtering algorithm with a coefficient of variation method, adopting the coefficient of variation method to analyze and simulate user ratings from multiple dimensions, calculating the similarity between oilfield cloud users according to the Jaccard formula, calculating the user's interest in the oilfield cloud application component by using an interest calculation formula, and the interest value is the user's recommendation result.

The invention provides an agent-based multi-model collaborative evaluation method and related equipment, and the method comprises the steps: carrying out the semantic analysis of a natural language task request inputted by a user, decomposing an analysis result, and generating a corresponding task graph; calling a worker agent according to a subtask sequence of the task graph, obtaining candidate answers output by different language models, and generating an answer envelope set; projecting the embedded vectors of the answer envelope set to a unified semantic space, and performing weighted fusion on the embedded vectors to generate an aggregation vector; performing quantitative scoring on the answer envelope set and the aggregation vector to generate a multi-dimensional scoring matrix and candidate answer subsets; and carrying out aggregation calculation on the candidate answer subsets according to the multi-dimensional scoring matrix, determining global confidence distribution, and carrying out information supplementation on a low confidence region to generate a final candidate answer. According to the method, the reliability of the question-answering system is effectively improved by performing collaborative evaluation and unified aggregation on multi-model output.

The present invention discloses an item recommendation algorithm based on latent features and ratings, including: processing input data, extracting user rating data, and constructing a user-item rating matrix; introducing an activity penalty factor to calculate the similarity of a collaborative filtering algorithm; introducing a time factor and a penalty factor to penalize active users, and calculating the Pearson similarity based on time weight; based on an introduced item popularity weight factor, using an exponential function to replace the popularity difference between two items and integrating it into the Pearson similarity calculation; using the weighted sum of the two similarities as the final similarity, and sorting from large to small, obtaining the first k items that are most similar to the item to form a similar item set; introducing a time weight function, calculating the user's predicted interest in unrated items and sorting them, and recommending the first N items to the user. The present invention can realize personalized recommendations, improve the accuracy of recommendations and the user's experience.

The application relates to a privacy protection recommendation method and system based on matrix decomposition and belongs to the technical field of privacy protection recommendation. The method comprises the following steps: obtaining a historical rating matrix of a user; performing matrix decomposition on the historical rating matrix to obtain a shared rating matrix; sending the shared rating matrix and a scored item set to a recommendation server to generate public parameters, a decomposition matrix and a shared value of the decomposition matrix; generating inner product triplets and multiplication triplets by using a triplet server; calculating errors between real ratings and predicted ratings by using the recommendation server to generate an error shared matrix; optimizing the error shared matrix until the error squares of all the error shared matrices are less than a threshold value; calculating partial derivatives of a loss function on the decomposition matrix to obtain a gradient shared matrix; updating the decomposition matrix to obtain a user decomposition shared matrix; and generating a privacy protection recommendation result of the user by using the user decomposition shared matrix. The application provides strong protection in the aspect of protecting user privacy.

The present invention provides a tensor-based hybrid recommendation method and system, belonging to the field of data processing technology, comprising: converting user-side information, project-side information, and time-side information into discrete data information; then inputting the information into user, project, and time tensor neural networks for feature extraction to obtain user latent factor matrices, project latent factor matrices, and time latent factor matrices; performing a paradigm multivariate decomposition on a third-order rating tensor to extract the latent factor matrices, thereby obtaining user latent factor matrices, project latent factor matrices, and time latent factor matrices; after fusing the two user latent factor matrices, project latent factor matrices, and time latent factor matrices, the fused factor matrices are restored using the inverse operation of the paradigm multivariate decomposition to obtain a final approximate rating matrix. The present invention, through a tensor-based hybrid recommendation algorithm, fully considers both linear and nonlinear information, thereby improving the accuracy of recommendation results.

The invention discloses a recommendation method based on interpretable generalized logic transformation matrix decomposition. The recommendation method comprises the following steps: converting an original scoring matrix into normal distribution data through a generalized logic transformation function; constructing indexes based on similarity and indexes based on ranking; calculating probability distribution and expected scores of scores of the recommended items by the similar users, and generating interpretability indexes in combination with the similarity indexes; integrating the interpretability index into a matrix decomposition objective function for optimization; solving a user feature matrix and a project feature matrix through an alternating least square method or stochastic gradient descent; calculating a prediction score and mapping the prediction score back to an original score interval through generalized logic inverse transformation; generating a recommendation list; the method provided by the invention has wider applicability and higher performance in practical application.

The invention discloses a digital marketing recommendation method and system based on big data, and relates to the technical field of commodity recommendation. The method comprises the steps of obtaining original data of an e-commerce platform, obtaining a structured data set through preprocessing, performing fuzzy clustering on a scoring matrix, generating and iteratively updating a membership matrix, reducing the influence of initial clustering deviation, calculating clustering attribution in combination with user geographic information, and correcting scores according to the clustering attribution to obtain a final scoring matrix. Inputting the matrix and the user and commodity feature vectors into a recommendation model, and outputting a commodity recommendation result; clustering multiple membership degrees of the users by adopting fuzzy clustering, and adapting to complex overlapping characteristics of user preferences under sparse data of the distributors; the initial deviation is reduced by iteratively optimizing the membership degree, attribution is determined in combination with geographic information, and the clustering fitness and distinction degree are improved; the commodity recommendation model effectively solves the problem of low accuracy of the commodity recommendation result under the condition of reducing the calculation complexity.

This invention discloses a graph mining-based questionnaire analysis method, system, electronic device, and medium, relating to the field of data processing. The method includes: acquiring questionnaire data to be analyzed; establishing a questionnaire matrix based on the questionnaire data; cleaning the questionnaire matrix to obtain a scoring matrix; calculating the similarity between different attribute scores based on the scoring matrix to establish a core attribute adjacency matrix; using attributes in the core attribute adjacency matrix as nodes in the network to establish a core attribute network; performing graph mining on the core attribute network to obtain a minimal core attribute network; and analyzing the influence of different core attributes based on the minimal core attribute network to obtain the questionnaire analysis results. This invention can reduce the influence of extreme data in questionnaires and improve the interpretability of the data in questionnaires.

The application discloses a fair personalized recommendation method and device based on mutual information decoupling and a storage medium, and steps of the method comprise the following steps: 1. constructing original data, including a user-product rating matrix and a user sensitive attribute matrix; 2. constructing a sensitive embedding network to learn sensitive embedding of a user and a product, including a biased one-hot encoding layer, a sensitive information encoder and a sensitive attribute prediction layer; 3. constructing a hybrid embedding network to learn hybrid embedding of the user and the product, including a hybrid one-hot encoding layer, a hybrid information encoder and a preference prediction layer; and 4. constructing a non-sensitive embedding network to learn non-sensitive embedding of the user and the product, including an unbiased one-hot encoding layer, a mutual information lower bound optimization layer and a mutual information upper bound optimization layer. Through the double mutual information fairness constraints on the embedding vectors, the application improves the fairness of the recommendation system while ensuring the recommendation accuracy.

The application provides a multi-field content recommendation method and system with novelty fusion, and the application optimizes a user-item rating matrix based on a BiasSVD model, adds a novelty factor to predict the user rating matrix, and alleviates problems such as unsatisfactory recommendation effect caused by data sparseness in a traditional model; similarity is calculated based on user feature information, cold start problems caused by excessive dependence of a traditional recommendation algorithm on historical data are solved, the recommendation result is closer to the real rating, and the recommendation accuracy is improved; the novelty relationship between users is introduced, and the novelty of the recommendation result of a traditional collaborative filtering recommendation algorithm is improved. The method disclosed in the application can alleviate the problem of sparse rating data, and can improve the novelty of the recommendation result, so that the recommendation result is not too single, thereby avoiding user aesthetic fatigue.

This invention proposes a rating-based reliable recommendation system, which includes: acquiring and cleaning data from a database to obtain the required information; classifying users and items with different preference levels according to a noise identification rule based on user preference consistency, and identifying noise in the original rating matrix; generating an initial rating reliability matrix according to a designed rating reliability matrix generation module; filling non-interactive areas in the rating reliability matrix according to a designed positive sample filling rule to balance the data; using a generative adversarial network to train the acquired rating matrix and reliability matrix to predict the rating value and reliability probability of each blank area; filtering predicted ratings with reliability probabilities below the system threshold by comparing them with a reliability threshold, and retaining ratings with higher reliability probabilities; sorting the filtered predicted rating set, and pushing the top k items with higher predicted ratings to the target user to form their personalized recommendation list.

The application discloses an online course recommendation method and device based on class concentration detection, which first collects basic information data of students, vectorizes the basic data of the students, and generates an initial recommendation list by using a user-based collaborative filtering method; then, concentration detection is performed on video records of the students during class, the proportion of the concentration state is calculated, and the proportion is taken as a score of the students on the course; finally, the existing score is converted into a matrix form, a recommendation result is obtained by using a matrix decomposition-based collaborative filtering method, and the recommendation result is fused with the initial recommendation result to obtain a final course recommendation result. The application combines the concentration of the students during class, can better feedback the interest degree of the students on the course, and obtains a more accurate student-course score; and the application can improve the sparsity of the student-course score matrix, and has a better effect when the matrix decomposition-based collaborative filtering method is applied.

The picture fuzzy set-based collaborative filtering recommendation model comprises the following steps: obtaining and cleaning data information from a database to obtain an original rating matrix; based on picture fuzzy set theory, converting the rating matrix into four matrices representing different preference degrees of users, namely, a membership matrix, a neutral matrix, a non-membership matrix and a rejection matrix; fitting the preference matrix by using a BeMF model to learn feature vector information of the users and the items, and obtaining picture fuzzy numbers of the users for the rated items through inner product of the feature vectors; scoring the picture fuzzy numbers by using a scoring function; learning potential global user preferences in historical ratings by using a multilayer perceptron; combining the scoring of the picture fuzzy numbers (fine-grained preference) and the output value of the multilayer perceptron (global preference) to form a comprehensive prediction value and sort the comprehensive prediction value to generate a recommended item list.

The invention provides a talent competency assessment method and system. The talent competency assessment method comprises the steps that 1, related data of talents are collected, the collected data are preprocessed, a knowledge base for talent competency assessment is constructed, knowledge in the knowledge base is subjected to structured processing, and multi-granularity knowledge used for assessment is formed; 2, constructing an original scoring matrix; 3, constructing an ideal solution according to the original scoring matrix; 4, constructing a utility value matrix; 5, establishing a regret-appreciation matrix; step 6, establishing a perception utility value matrix; 7, determining an attribute weight through an entropy weight method; and step 8, calculating a comprehensive evaluation risk value, sorting the objects through the comprehensive evaluation risk value, and the larger the comprehensive evaluation risk value is, the stronger the competency is. According to the method, the competency of the talent is evaluated in a data and knowledge cooperative driving mode, the problems of single data and insufficient knowledge application in a traditional evaluation method are solved, and comprehensive and accurate evaluation of the competency of the talent is realized.

The application discloses a time-aware adaptive interest point recommendation method based on K-means clustering, which comprises the following steps: first, collecting and sorting check-in data sets, and converting to generate a user-time-location three-dimensional score matrix; second, extracting a two-dimensional check-in score matrix in each time slot, and generating a one-dimensional score vector of each time slot by using a data compression technology; based on the one-dimensional score vector, the K-means method is used to cluster the time slot; third, calculating the dynamic similarity of users in each time slot; based on the time clustering, the score method of the traditional user-based collaborative filtering algorithm is improved, so that the interest point prediction score can be adaptively generated according to the current recommendation time; a plurality of unvisited addresses ranking at the front at the current time are recommended to the user; fourth, the recommendation quality is evaluated by using a recommendation precision index, and the accuracy and effectiveness of the proposed technology are evaluated by comparing the recommendation precision of the technology proposed by the application with that of other classical recommendation systems.

The invention relates to the technical field of commodity recommendation, in particular to a personalized commodity recommendation method and system based on a collaborative filtering algorithm. Analyzing and processing the user data; constructing a user and commodity scoring matrix; calculating the similarity between the users, and generating a user set with high similarity; generating a recommended commodity table through the user set with high similarity; displaying the recommended commodity to the new user; user feedback is created to facilitate subsequent optimization; according to the method, positive feedback commodity recommendation in the similar user set is recommended to the new user, and similarly, new products are also recommended to the user set with high similarity, so that the use experience of the new user is improved, and the popularization of the new products is promoted.

The invention discloses a commodity collaborative filtering recommendation method and system based on user behavior analysis, and relates to the field of commodity recommendation, and the method comprises the steps: classifying commodities, and building life cycle files with differentiated attenuation parameters for different categories; when recommendation calculation is carried out, a dynamic score with timeliness is calculated for each user-article interaction according to an interval between behavior occurrence time and current time in combination with a category life cycle file, and a dynamic score matrix is formed. And carrying out subsequent article similarity calculation and recommendation list generation based on the matrix. In this way, attenuation speeds and modes of different commodity interests can be effectively distinguished, the recommendation result can more accurately capture the current core demand of the user, and the problem that recommendation is inaccurate due to the fact that user interest dynamics, periodicity and category differences cannot be processed in the background technology is solved.

The application discloses a recommendation method based on an interpretable generalized logistic transformation matrix decomposition, and comprises the following steps: converting an original score matrix into normally distributed data through a generalized logistic transformation function; constructing a similarity-based index and a ranking-based index; calculating a probability distribution of a similar user's score on a recommended item and an expected score, and combining the similarity index to generate an interpretability index; integrating the interpretability index into a matrix decomposition target function for optimization; solving a user feature matrix and an item feature matrix through an alternating least squares method or a stochastic gradient descent; calculating a predicted score and mapping back to an original score interval through a generalized logistic inverse transformation; and generating a recommendation list; the application has wider applicability and higher performance in practical application.

The invention provides a product recommendation method which can be applied to the technical field of artificial intelligence. The product recommendation method comprises the steps of obtaining user information of a plurality of users, wherein the user information at least comprises a plurality of user features of the users; generating a preference value of each user for each candidate product based on the access frequency and the access time of each user for accessing each candidate product; based on the user features corresponding to the users and the preference values of the users for the candidate products, a score matrix corresponding to the target user is constructed, the target user is at least one of the multiple users, and the score matrix represents the probability that the target users like to access the candidate products; and on the basis of a preset collaborative filtering model, predicting at least one recommended product of the target user from the candidate products according to the scoring matrix. The invention further provides a product recommendation device, equipment and a storage medium.

The application provides a popularity debiased recommendation model based on item similarity, which comprises the following steps: extracting user item rating data from a database and preprocessing the data to obtain an original rating matrix; calculating the similarity between items based on KL divergence, and calculating the popularity based on item similarity (ISP) based on the similarity; based on the causal theory, decoupling the interaction score of the user and the item into two parts of "real interest" and "herd behavior", and assigning corresponding embedding vectors to the user and the item; by introducing a weight attention mechanism, adaptively learning the weight of the user's interest and herd behavior; based on the obtained weight, weighted sum of the two parts of the score, calculate the final personalized recommendation score; by using the negative sampling method based on ISP, the training data set is divided, and the loss function is constructed to supervise the learning of the decoupled embedding vector; predicting and ranking the score of the item that the user has not rated, and selecting the top k items to form a personalized recommendation list.

The application particularly relates to a post-fusion personalized recommendation model and method based on explicit and implicit feedback features, the model comprising an explicit feature extraction module, an implicit feature extraction module and an overall feature extraction module, the explicit feature extraction module and the implicit feature extraction module being connected with the overall feature extraction module respectively; the method fuses an IBPR model and a BiasSVD model, performs weighted summation on a predicted score matrix obtained by the BiasSVD model and a predicted ranking score matrix obtained by the IBPR model, obtains a final predicted ranking score matrix, ranks all predicted ranking scores of users from high to low, and recommends the first items with higher ranking to the users. The application extracts explicit feedback features by using the BiasSVD model, extracts implicit feedback features by using the IBPR model, and fully utilizes historical score data and implicit feedback data in a data set, thereby relieving a cold start problem of a recommendation system and improving the performance of the recommendation system.

The application relates to a recommendation system attack detection sample data generation method and device. The method comprises the following steps: obtaining a real user score matrix in a data set of a recommendation system; generating a false user score matrix corresponding to the real user score matrix through a generator according to the real user score matrix; mixing the false user score matrix and the real user score matrix to obtain a mixed user score matrix; simulating attacks on a plurality of recommended items through a simulated recommendation system attack discriminator according to the mixed user score matrix to obtain an attack loss; and generating an attack detection sample based on the attack loss and the mixed user score matrix. The method can support accurate detection of the attack resistance of a recommendation system.

The application discloses a recommendation algorithm based on serial auto-encoders, comprising 1) merging item-based rating information and user and item interaction auxiliary information into an auto-encoder for reconstruction output, obtaining a feature representation of the reconstruction output through traditional auto-encoder learning, using auxiliary information to help the original rating matrix to reconstruct, and reducing the loss of effective information; 2) designing a serial connection method of auto-encoders, obtaining the reconstruction output generated by the first auto-encoder, inputting the reconstruction part of the output to the original rating matrix into the second auto-encoder, comparing the output of the second auto-encoder, i.e., a predicted rating matrix, with the original rating matrix, and calculating the prediction accuracy. The application can utilize attribute information of items, process the attribute information through auto-encoders, use the attribute information as extended features of recommendation, and achieve the purpose of more accurate recommendation for users.