329 results about "Rating matrix" patented technology

According to various embodiments, a ratings matrix including matrix values is generated, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items. The ratings matrix may include a missing entry representing an unknown affinity rating. According to various embodiments, a revised ratings matrix is generated by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry.

A system and method of providing personalized item recommendations in a communication system comprising a server and a plurality of client devices. At the server, a plurality of user rating vectors are received from a plurality of client devices and aggregated into a rating matrix that is factorized into a user feature matrix and an item feature matrix, with the product of the user feature and item feature matrixes approximating the user rating matrix. The factorization comprises the steps of the ALS1 or the IALS1 algorithm including: initializing the user feature matrix and the item feature matrix with predefined initial values; alternately optimizing the user feature matrix and the item feature matrix until a termination condition is met. The item feature matrix is transmitted from the server to at least one client device, and a predictive rating vector is generated as the product of the associated user feature vector and the item feature matrix. At least one item is selected for recommendation to a user from the items associated with the predictive rating vector.

The invention discloses an internet information product recommending method based on matrix decomposition. The method comprises the following steps of: 1) obtaining the user scoring record to the information product; 2) obtaining the social relationship record between internet users; 3) respectively building a scoring matrix and a social matrix according to the types of a target user and a target product; 4) learning a user feature vector and a product feature vector through a matrix decomposition technology; 5) calculating the scores of different products scored by the target user according to the feature vectors, so as to recommending the favorite products of the user according to the scores. In the method, analysis on user social relationship is introduced, and personalized product recommendation is provided for the target user based on the production type information. The calculation is simple and quick, and the method has better expandability and adaptability, so that the method is suitable for highly dynamic and immense amount of product-oriented recommendation for the internet users.

The invention discloses a cold-chain logistic stowage intelligent recommendation method based on spectral clustering. Scores of users for a stowage line are conveyed through a cold chain for cold-chain logistic stowage intelligent recommending, a score matrix is built, the Euclidean distance is used for calculating the user similarity, a degree matrix is used for calculating a Laplacian matrix, feature vectors are obtained by calculating feature values of the orderly Laplacian matrix, a K-means algorithm is used for clustering the feature values to obtain a user group with the similar interesting stowage line, and a stowage line is recommended inside the user group with the similar interesting stowage line, so that cold-chain logistic stowage intelligent recommending is achieved, the cold-chain logistic vehicle non-load ratio is lowered, and the profit rate of cold-chain logistic transport vehicles is increased.

A method and apparatus for performing discriminative training of, for example, call routing training data (or, alternatively, other classification training data) which improves the subsequent classification of a user's natural language based requests. An initial scoring matrix is generated based on the training data and then the scoring matrix is adjusted so as to improve the discrimination between competing classes (e.g., destinations). In accordance with one illustrative embodiment of the present invention a Generalized Probabilistic Descent (GPD) algorithm may be advantageously employed to provide the improved discrimination. More specifically, the present invention provides a method and apparatus comprising steps or means for generating an initial scoring matrix comprising a numerical value for each of a set of n classes in association with each of a set of m features, the initial scoring matrix based on a set of training data and, for each element of said set of training data, based on a subset of said features which are comprised in the natural language text of said element of said set of training data and on one of said classes which has been identified therefor; and based on the initial scoring matrix and the set of training data, generating a discriminatively trained scoring matrix for use by said classification system by adjusting one or more of said numerical values such that a greater degree of discrimination exists between competing ones of said classes when said classification requests are performed, thereby resulting in a reduced classification error rate.

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.

A recommendation method based on collaborative filtering is provided, which includes the following steps. A target user identifier (ID) is acquired. A user group ID corresponding to the target user ID is searched. A similarity between items is acquired, which is determined according to a user-item rating matrix corresponding to the user group M. An item is recommended to a target user according to the similarity between the items.

System and method for performing Collaborative Filtering while preserving complete user anonymity are provided. Each of a group of client devices sends a rating vector anonymously to a server. The cells in each rating vector correspond to a set of items, and selected cells have ratings provided by the user associated with the corresponding client device for the corresponding items. The server aggregates all the rating vectors into a rating matrix, and factorizes the rating matrix into a user feature matrix and an item feature matrix through approximation, such that the rating matrix equals the product of the user feature matrix and the item feature matrix. The item feature matrix is sent to the client devices. Each of the client devices calculates its own user feature vector based on its rating vector and the item feature matrix, and provides personalized recommendations on selected items based on the client's user feature vector and the item feature matrix.

The invention relates to a commodity recommendation method and device based on an electronic business platform and a server. The commodity recommendation method and device based on the electronic business platform and the server are used for improving the accuracy of individual commodity recommendation. The method comprises the steps that adhesive user groups are screened; user interestingness is acquired; a neighbor set closest to a target user is generated; a commodity recommendation list is generated. According to the technical scheme, the obtained adhesive user groups can be more accurate and efficient; due to the fact that only the interestingness of each user in the adhesive user groups is recorded in an obtained scoring matrix, an evaluation matrix can be filled, and the density of the scoring matrix is improved; the closest neighbor set between the target user and the other users in the adhesive user groups is calculated through the scoring matrix, the similar neighbors of the target user can be searched for more accurately, and then the accuracy of recommending commodities to the target user is improved.

The invention discloses a collaborative recommendation method based on social context regularization. The collaborative recommendation method comprises the following steps of: 1) firstly, extracting a user object matrix and a socialization relation matrix, wherein during the collaborative recommendation, the user object matrix is defined by using a grading matrix of a user on an object, a clicking frequency of the user on the object or a visit relation, and the socialization relation is a relation, generated by some behaviors of the user, between the user and other users in the system; 2) filling the user object matrix by using a low-rank matrix decomposition method with the social context regularization and recommending N objects to each user by using a result matrix; and 3) adjusting the weight of the social context restraint during matrix decomposition in the consideration of difference among different users. By the method, the problems of single recommended information of the conventional collaborative filtering recommendation algorithm and inaccurate recommendation result caused by dilution of the user object matrix are solved; furthermore, compared with the conventional method, the method has the advantage of obviously enhancing the recommendation result accuracy.

The present invention provides a collaborative filtering recommendation method based on typicality and a trusted network. According to the method, an original sparse scoring matrix is replaced with a dense user typicality matrix and a project typicality matrix, and a user-trusted network is also used to improve a traditional collaborative filtering recommendation algorithm. A sparseness problem referring to little user score data in the traditional collaborative filtering recommendation method is solved by using the project typicality matrix in a project set and the user typicality matrix in a certain project set that the user is interested in, the user-trusted network is also used to further improve the recommendation accuracy, and data dimensionality deduction is realized. The recommendation result can fully use the impact of a user social trust relationship on similar user interests.

An embodiment of the invention discloses an information recommending method and device, wherein the method includes: selecting K neighboring users from a social relation network by learning embedding feature vectors of users in the network according to node2vec algorithm, and extracting potential features of a current user from an embedding feature matrix generated from the K neighboring users according to CNN (convolutional neural network) algorithm; subjecting a historical information rating matrix to iterative alternating operation via a preset algorithm according to the potential features so as to obtain a user feature matrix and information feature matrix of the current user; performing information recommending for the current user according to the user feature matrix and/or information feature matrix. Deep potential features of a current user can be mined so that information recommending efficiency and accuracy are improved.

The invention relates to a social association cloud media collaborative filtering and recommending method. The method includes the following steps that micro blogs sent by multiple micro blog users and associated users of the micro blog users are obtained; a user program rating matrix for reflecting the corresponding relation between different users and grading of different programs is built; influence grading of the associated users on the programs is calculated; the feature vector of the micro log users is calculated; feature similarity of the micro log users is calculated; the influence grading of similar users similar to the micro log users on the programs is calculated; the user program grading matrix is updated according to the influence grading of the associated users on the programs and the influence grading of the similar users on the programs; network resources are explored, and the updated user program grading matrix is expanded; cluster is conducted on the user program grading matrix based on the users and the programs respectively; class cluster obtained through the cluster serves as a neighbor search domain, and grading is predicted through collaborative filtering and recommending. By means of the method, network information content which interests the users can be accurately recommended for the users.

The invention relates to a video recommendation method based on timing sequence data mining. The method comprises the steps that 1) video interest gradient change of a user is analyzed via third party data and a user interest gradient curve is obtained, and the singular points of the user interest gradient curve act as time points of user interest migration; 2) the latest interest migration time points of the user are confirmed, and user-item scores after the latest interest migration time points of the user are acquired so that a user-item score matrix meeting the current interest of the user within a selected time window is established; and 3) user customized video recommendation is performed by using a random walk model on the basis of the user-item score matrix. An interest migration problem in the customized video recommendation is considered, and customized video recommendation is performed though integration of a time window method and on the basis of the Random Walker trust degree model so that video recommendation accuracy and efficiency are enhanced.

The invention discloses a recommended system and a method with facing social network for context awareness based on the tensor decomposition, and relates to the field of the data mining and the information retrieval. Firstly, the method makes use of a social network massive data set to collect users and projects and contexts, to pay attention to the list information, to establish an original the user-the project-the context mark matrix, to calculate the users similarity, and to establish a user-user similarity matrix; Secondly, aim at the extreme sparsity of the original mark matrix, a sparse mark matrix is predicated and filled by using the tensor decomposition; Thirdly, aim at a problem that the user similarity matrix is sparse, a sparse user similarity matrix is predicated and filled by using the matrix decomposition; Finally, according to some similar interest tendencies of some similar users in the social network, a social normalization item is taken to optimizing the mark matrix. The method deals with the problem that a traditional predicated mark matrix does not consider that the context information and the relationship between users have an effect on marking. Also, the method deals with an obstruction which is caused by the sparsity of the mark matrix brings to the recommended system, thus the accuracy of the recommended system is improved. The method can be widely applied to the fields of the social network, the electronic commerce and the like.

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.

The invention relates to a dynamic recommendation method capable of adapting to user interest changes based on time information. The method comprises the steps that a user-object explicit rating matrix is built; a user-object implicit rating matrix is built; a user-object comprehensive rating matrix is built; similarity between two users is calculated; K users close to the target user similarity are obtained as the neighbour set of target users; a monotone decreasing exponential time function is selected as a rating weighting function, and the weighting factor of each user in the rating weighting function is calculated according to different interest change trends embodied by the scores of the users; a TOP-N recommendation method is adopted, and N objects with very top predication scores are recommended to the users. According to the method, the changes of the user interest along with time are considered, and the more precise personalized object recommendation service is provided for the users.

The invention discloses a shop recommendation method based on a position of a mobile user. The method comprises sending position information of the user to a server by a mobile phone used by the user, finding all shops related to the position by the server according to the position information sent by the user, finding all users related to the shops, and finally generating a user-shop scoring matrix; and the method further comprises initially filling the obtained user-shop scoring matrix, performing similarity calculation on a target user vector and other user vectors in the obtained matrix, generating neighbor users, performing scoring prediction on an unscored shop of the target use according to the neighbor users, and performing recommendation for the user according to a prediction result. After prediction scores of the unscored shops of the target user are obtained, the recommendation is performed for the user; and all the prediction scores are ranked, and then the shops, which have the high prediction scores and are the former N items, are recommended to the user.

The invention provides a kernel method-based collaborative filtering recommendation system and a kernel method-based collaborative filtering recommendation method. The corresponding system comprises a data preparation module which is used for standardizing the original data and carrying out corresponding preprocessing, generating a user-project rating matrix and a project distance matrix to output; a user interest modeling module which is used for constructing an interest model for a user on a project space according to the user-project rating matrix and the project distance matrix as well as a kernel density estimation technology; and a recommendation result generation module which is used for computing the similarities among the users according to the interest model, generating a neighbor set of a target user, and predicting a score of the project rated by the user according to a predetermined recommendation strategy and returning the recommendation result. Through the recommendation system and the recommendation method provided by the invention, the user interest model can be better presented, the user similarity in the practical application is estimated more accurately, the performance of the recommendation system can be promoted considerably, and more stable recommendation result can be obtained.

A method of doing business is disclosed for the sale of insurance options. An individual is assigned a Health Risk Score (HRS); in a preferred embodiment, the HRS is determined by using an automated case-based method for assessing risk. The individual may then purchase an option for insurance coverage that will begin only after a predetermined period of time. At the beginning of that period, the individual's HRS is re-evaluated, and if it remains within predetermined acceptable limits, the individual may then obtain insurance coverage with premiums and benefits defined at the time of the purchase of the option. A case-based method for assessing risk is disclosed, in which various risk factors and their combinations are used to provide a scoring matrix. This scoring matrix is then used to evaluate the individual's HRS.

The invention provides a mixed recommendation method based on a factorization condition limitation Boltzmann machine. The method includes: constructing a target user feature vector; building a target user grading matrix set; building a target user training sample; building a factorization condition limitation Boltzmann machine model; grading and predicting to recommend results to a target user. The problems of user interest expression inaccuracy caused by pure recommendation based on content and data sparsity caused by recommendation based on behavior are effectively solved.

The invention discloses an item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm. The method comprises the following steps of obtaining the information of interest of users on every item and establishing the score matrix of every user on all the items; calculating the average score of every user, the quantity of the scoring users of every item and the average score of every item; calculating a common comment user quantity matrix; calculating the Pearson similarity and the modified cosine similarity of between any two items; calculating the similarity based on explicit feedback; calculating the cosine similarity based on implicit feedback; calculating a final similarity; obtaining the nearest neighbor set I of a current item; when providing a recommendation list to a target user u, according to the score matrix, obtaining the scored items and the unscored items of the target user u; calculating the prediction scores of the unscored items of the target user u and selecting N items with the highest scores inside the unscored items of the target user u to the user. The item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm can effectively improve the accuracy of prediction recommendation.

The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.