214 results about "User modeling" patented technology

The present invention is directed generally to providing systems and methods for data analysis. More specifically, embodiments may provide system(s) and method(s) including dynamic user modeling techniques to capture the relational and dynamic patterns of information content and/or users' or entities' interests. Various embodiments may include system(s) and method(s) that are based on, for example, the past history of content semantics, temporal changes, and/or user community relationship. Various embodiments may include modeling and/or analysis of the dynamic nature of an item of interest's value to a user(s)/entity(ies) over time. The dynamic factors may be consider in any manner, such as, individually or combined, sequentially or simultaneously, etc. Further, some embodiments may include, for example, system(s) and method(s) relating to analyzing data to capture user/entity interests and/or characteristics, consider content semantics and evolutionary information, and/or using community relationships of users/entities to thereby analyze information and provide dynamic conclusion(s) (e.g., recommendation(s)).

A method and apparatus for utilizing user behavior to immediately modify sets of search results so that the most relevant documents are moved to the top. In one embodiment of the invention, behavior data, which can come from virtually any activity, is used to infer the user's intent. The updated inferred implicit user model is then exploited immediately by re-ranking the set of matched documents to best reflect the information need of the user. The system updates the user model and immediately re-ranks documents at every opportunity in order to constantly provide the most optimal results. In another embodiment, the system determines, based on the similarity of results sets, if the current query belongs in the same information session as one or more previous queries. If so, the current query is expanded with additional keywords in order to improve the targeting of the results.

A data leak protection method for managing user interaction with a computing device, the computing device comprising a kernel mode of operation and a user mode of operation, the method comprising: monitoring the kernel mode of the computing device in order to detect user-initiated events; determining whether a given user-initiated event has a forbidden status or an allowed status; performing an action in dependence on the status of the given user-initiated event.

The present invention is directed to systems and methods for data and/or information analysis. The systems and methods may be directed to knowledge management and/or user modeling. In various embodiments, the systems and methods may utilize relational representations and/or evolutionary representations of information. For example, expertise information and/or evolutional information related to expertise information may be analyzed and representations presented indicating relationships and temporal evolution.

The invention relates to an individuation catering recommendation method and system based on multiple targets. The individuation catering recommendation method mainly includes the steps that essential information and behavior information (including diet records and browsing behaviors on webpages) of a user are collected and recorded into a database; a user model is built according to user information in the database, and individuation nutrition recipes are recommended; nutrition balanced diet recipes are generated through multi-target optimization catering models, the similarity between the recipes based on nutrient elements is calculated through a collaborative filtering recommendation model and an equivalence interchange model, various interchange recipe lists are generated from recipe bases, and the dietary structure is enriched; the nutrition balance of the recipes is detected, improvement measures are provided by comparing practical contents and recommended nutrient intakes of various nutrients in the generated recipes, and the designed recipes trend to be reasonable.

The systems and methods described herein can be used to provide virtual service environments. In one embodiment, a virtual service model is generated by detecting one or more transactions, each of which includes a request sent from a requester to a software service and a response sent from the software service to the requester; storing information describing the detected transactions in a virtual service model, where the information describing each transaction includes information identifying a command included in the request and information identifying a response attribute included in the response; and generating information describing an unknown transaction, where the information describing the unknown transaction includes information identifying a first command and information identifying a first response attribute. The first command and the first response attribute are copies of a corresponding command and a corresponding response attribute associated with a corresponding one of the detected transactions.

The invention provides a dish recommendation system based on data mining and cloud computing service. The method comprises the following steps: 1) receiving a recommendation request input by a user; b) judging whether the user selects a restaurant, and if not, recommending at least one restaurant for the user; c) reading a restaurant template according to the selected restaurant or the recommended restaurant, so as to generate at least one dish collocation scheme based on a dish model; d) scoring the generated at least one dish collocation scheme, wherein scoring standards are selected from at least one of special dish matching judgement, user historical behaviour and evaluation judgement, food material repeatability judgement and user collaborative recommendation weighting; e) selecting a scheme with the highest score and recommending the scheme to the user; f) judging whether the user is satisfied with the recommended scheme, and if not, recommending again until the user is satisfied; and g) updating the dish model, the restaurant model and a user model based on the scheme which the user is satisfied with or not.

The invention discloses a personalized recommendation method and system based on a probability model and user behavior analysis. The method includes the steps that article information and article attribute information are extracted, and operation behaviors of users on articles are extracted; interest points are obtained according to the article attribute information and the operation behaviors of the users on the articles; user interest similarity is obtained according to the operation behaviors of the users on the articles, and similar users are obtained; a decay factor is obtained according to the operation behaviors of the users on the articles based on the time dimension, and a user model is set up; interest characteristic information, at all dimensions, of the users is obtained according to the user model; after filtering, a recommendation algorithm is adopted to generate results to be recommended, and algorithm fusion is conducted to obtain personalized recommendation results of the users. After original data is preprocessed, the user model is set up, the interest points of the users and essential information acquisition requirements are depicted accurately to provide accurate personalized recommendation, and therefore the problems of information overload and long-tail articles in the network are solved.

The invention provides a question pushing method and system, which are implemented based on pre-established user models, and the user models include at least two of the following models: an interest model, an attribute model, a behavior model and a relation model, wherein the interest model is established by exploiting questions asked by users and answers thereof, the attribute model is established based on user attributes, the behavior model is established by carrying out statistics on user behaviors, and the relation model is established based on relations between different users. The method comprises the following steps: A, carrying out text analysis on a question to be answered so as to extract question features; B, matching with the user models by using the extracted question features, and carrying out sequencing on users according to the matching degree of the question features with the users in each user model and a preset sequencing weight of each user model; and C, pushing the question to be answered to the front M users in the obtained sequence, wherein M is a preset positive integer. Through the method and system disclosed by the invention, the answer providing efficiency and quality of a knowledge questioning and answering system can be improved.

The invention discloses a method for establishing a user interest model. In the method, on the basis of constructing an user individual interest model, a social coordination relationship among users is calculated by utilizing behavior characteristics of the users, a group interest model of the users is established by taking a quantization value of the relationship as a weight, and finally the group interest model is fused with the individual interest model to form the final interest mode. The method can be used for user modeling in various personalized pushing services, such as personalized news and book recommendation, in application such as Internet and an E-commerce system.

The invention discloses a multi-user model moving track prediction method based on sequential pattern mining. Transverse track data positioned through mobile phone signaling is utilized to establish multiple user models to predict. According to the multi-user model moving track prediction method based on sequential pattern mining, a stay point mining algorithm with outlier tolerance is improved and provided, and stay points in the track are effectively mined; an inter-user similarity index based on a user stay area is provided, based on the index, transverse user track data is divided, and different user prediction models are established; based on a PrefixSapn algorithm, a moving track prediction method based on a sequential mining mode is provided, and the prediction model can be updated through newly generated real-time tracks.

The invention provides an advertisement-putting-oriented potential user searching and user model ordering method. The method includes the following steps that S1, automatic tracking of tension clues is performed on users according to conditioned reflexes of the users in the process of browsing information; S2, reading habits of the users are recognized, and time-order character excavation oriented to the reading habits of the users is completed; S3, by using the tension tracking principle, layered user models are built and ordered; S4, advertisements are pushed to the users who are ranked in the top after the user models are ordered.

The invention belongs to the field of information technology, and particularly discloses an intelligent evaluation method and system for medical instrument B2B website users. The evaluation method comprises the steps of 1, user evaluation index base building; 2, evaluation rule base modeling, 3, evaluation executing and 4, output and feedback. The evaluation system comprises four corresponding modules which execute the four steps respectively. The basic information, historical transaction information and online comment information of the medical instrument B2B website users are synthetically computed and modeled, a quantized evaluation value is provided for each site user and used as a reference during online transaction, and therefore the purposes of evaluating the transaction concluding possibility and preventing potential risks are achieved. The intelligent evaluation method and system for the medical instrument B2B website users have the advantages that data mining, multiple regression modeling and natural language processing technologies are comprehensively utilized for site user modeling evaluation objectively and comprehensively; a feedback mechanism is provided, an intelligent evaluation rule base can be continuously improved, and therefore evaluation results are more accurate.

The invention discloses a method and apparatus for constructing a user model, and an equipment control method and apparatus. The method for constructing a user model comprises: obtaining historical work data of equipment; according to the equipment historical work data, parsing user preference data; and adding the user preference data to a user body model established in advance, thereby forming a user model. The equipment control method includes: obtaining current user environment data; searching user preference data corresponding to the user environment data in a user model constructed in advance; and invoking equipment influencing the user environment data, thereby enabling the user environment data to be changed to be identical with the user preference data during the equipment work process. According to the invention, the user model containing the user preference data is constructed in advance; and according to the user preference data, the equipment in the user environment is controlled, so that the user environment meets the user preference.

The invention discloses a cooperative anti-jamming hierarchical game model and an anti-jamming learning algorithm. The cooperative anti-jamming hierarchical game model is characterized in that a useris modeled as a leader, jamming is modeled as a follower, the jamming always takes the maximum reduction of throughput of a system as an optimization objective, and the user utilizes the anti-jamminglearning algorithm to adjust an anti-jamming strategy continuously, so as to maximize the overall throughput of the system. The anti-jamming learning algorithm comprises the steps of: firstly, constructing a Stackelberg game model, wherein participants are all users and the jamming; secondly, generating initial anti-jamming strategies by all the users randomly, and selecting a jamming strategy bythe jamming according to the anti-jamming strategy of one user; thirdly, selecting one anti-jamming strategy by the user randomly, calculating a corresponding utility function and comparing utility functions, and updating the jamming strategy by the jamming as actions of the user change; fourthly, performing loop iteration until the anti-jamming strategies of all the users realize convergence or reach a set number of iterations; and finally, calculating throughput of the whole network and the anti-jamming utility functions. The cooperative anti-jamming hierarchical game model and the anti-jamming learning algorithm utilize the cooperative relationship among the users to improve the anti-jamming ability of the system.

A Peking Opera teaching system based on somatosensory interaction, including a data acquisition module, a data processing module and a user interface interaction module. The data acquisition module obtains the user's joint information and sound data through the somatosensory interaction device; the data processing module realizes the user's logical control of the system Display the user model synchronized with the user's actions, perform matching and feedback between the Peking Opera standard model and the user model, obtain user voice data, and perform matching and feedback between the standard singing voice and the user's audio; the user interface interaction module performs the operation interface through the recognition of user gestures Display and switching, Peking Opera teaching video display, action performance evaluation display and audio scoring display. The Beijing opera teaching system of the present invention operates through somatosensory operation throughout, has a high recognition rate, and does not need a keyboard or a mouse as inputs. The user model has a high synchronization rate with the actual user posture, and there is no jitter, frame skipping phenomenon, and it is not affected by the hardware limitation of sensory capture and the environmental noise during bone tracking.

The invention provides a convolution neural network-based music recommending system and method. The system comprises a music user modeling module for collecting historical behavior data of a music user and constructing a preference model of the music user; a music feature modeling module for obtaining a regression model; and a recommendation algorithm module for finding music objects matched withthe preference of the music user through the regression model and recommending the music objects to the music user. According to the system provided by the invention, deep learning is applied to the recommending system, semantic differences between song features and audio signals are effectively compensated and the problems such as "cold start" and the like in collaborative filtering are avoided at the same time, so that the accuracy of the recommending system is increased; and the contradiction between low training efficiency and a high timeliness requirement is solved by adopting a convolution neural network and the historical behavior information of the user and audio acoustic features are added to the model, so that the recommendation results are more in line with the preference requirements of the user and the user experience of the recommending system is improved.

A system, method and computer program product for implementing a thin hypervisor. The thin hypervisor does not have any association with a VM. The thin hypervisor serves as a wrapper over hardware capabilities of a processor. The processor has privileged low-level capabilities EPT VTX, AMD V and the ARM has virtualization technology. In order to use processor hardware capabilities and receiving root privileges, the system operates in a “super user” mode. The VM operates in a VM environment with kernel privileges in a user mode. A super user space is created in a safe mode. A user space application (or a process) is created. A user space virtualization framework library is used. These entities are separated by a user space and a kernel space. A thin hypervisor is implemented in the user space.

The invention discloses a method, user modeling equipment and a corresponding system for carrying out personalized recommendation for users. The method comprises the following steps: extracting information models of famous persons concerned by a user from an information model database, wherein the famous persons are persons meeting predetermined conditions in a social network, and information models established for the famous persons on the basis of a first modeling strategy are stored in the information model database; establishing a user model for the user according to the information models of the famous persons concerned by the user and a second modeling strategy; acquiring information which is extracted according to the user model and is suitable for the user and recommending the information to the user. According to the method, the user modeling equipment and the corresponding system disclosed by the invention, the user model established according to the information models of the famous persons concerned by the user can accurately represent interests of the user, and the information recommended for the user according to the user model can be accurately matched with the interests of the user, thus the information recommendation accuracy is high, and the cold start problem caused by shortage or absence of historical behaviors of users is solved.