2182 results about "Behavioral data" patented technology

A method, system, and computer program product for providing targeted advertising services are provided. The method includes gathering profile data, behavior data, and external data for users of communications devices accessing content over a network. The method also includes creating a database of demographic profiles based on the gathered data, receiving a request from an advertising entity to present an advertisement to a population of the communications devices based upon a set of business parameters, and identifying a demographic profile in the database that satisfies criteria set forth in the business parameters. The method further includes transmitting the advertisement to the communications devices associated with the demographic profile that satisfies the criteria set forth in the business parameters.

A method, system and computer program product are disclosed for evaluating a customer network value of a set of customers for a given marketing campaign, said method comprising the steps of storing for each customer static information, collecting for each customer, historical and time cumulative network behavior data, building a customer network map from network behavior data said map describing network relation between nodes, one node representing one customer, computing the social network parameters for the nodes of the network map, selecting the most relevant social network parameters according to the objectives of the given marketing campaign, and, computing for each customer a Customer Network Value Index (NVI) by combining the most relevant social network parameters and static information. When a static information profile is defined for an extended customer population, applying data mining techniques on this population allows estimating a probabilistic NVI for each customer in the extended customer population for which no NVI has been computed.

A method of detecting an intrusion into (or an anomaly in a behavior of) a target software system begins by instrumenting the target software system to generate behavior data representing a current observation or observation aggregate. The method then determines whether the current observation or observation aggregate warrants a second level examination; preferably, this determination is made by processing the current observation or observation aggregate through a first level detection algorithm that provides a first, provisional indication of a possible intrusion. If a result of executing the first level detection algorithm indicates that the current observation or observation aggregate warrants a second level examination, the method continues by processing the current observation or observation aggregate through at least one or more second level detection algorithms to provide a second, more definite, fine grain indication of a possible intrusion. The observation aggregates used by the first and second level detection algorithms may be the same or different. The first and second level detection algorithms may be executed in the same or different systems, machines or processors. The target software system operation may be suspended as the current observation or observation aggregate is processed through the one or more second level detection algorithms. A given action (e.g., sending an alert, logging the event, activating a countermeasure, or the like) may be taken if the result of the second level examination indicates a possible intrusion. Multiple algorithms may be executed together within a single examination level, with the individual results then analyzed to obtain a composite result or output indicative of intrusive or anomalous behavior.

A system is disclosed that uses behavioral data collected by ISPs to categorize particular ISP subscribers. The behavioral data may, for example, include the identities of particular web sites and/or web pages accessed by particular subscribers, the search queries used by the subscribers to conduct Internet searches, and/or other types of behavioral information. The ISP subscribers are assigned to particular behavioral categories or “segments” using a behavioral segmentation schema that maps particular subscriber behaviors to particular behavioral segments. The ISP subscribers may also be mapped to other segment types, such as demographic segments derived from off-line data about the subscribers. The subscriber-to-segment mappings are made available to content targeting entities via a query interface that, for example, supports queries of the form “what are the segments associated with IP address X?”

Methodologies, systems, components and software are provided that perform web analytics to measure visitor to consumer conversion continuously throughout surfing, through conversion and past completion of a purchase on-line. In accordance with at least one embodiment, such methodologies, systems, components and software may be utilized to determine efficacy of a plurality of parameters relating to one or more Transaction Related Offerings (TROs). In accordance with at least one embodiment of the invention, such methodologies, systems, components and software may be utilized to configure one or more Consumer Behavior Decision Models (CBDMs) and/or generate consumer behavior data.

KnowledgeByGo is a knowledge based Internet application service provider system (ASP), that tracks and analyzes browser behavior in real-time. Data analysis is delivered to the website owner or marketing agent to decide if a real time response or off line campaign needs to be initiated. KnowledgeByGo allows real-time behavioral tracking and prediction; customer relation management; one-to-one banner manager; site analysis reporting service; industry wide marketing research reports; product management; order processing; secure payment system; and customer contact manager. Subscribers get the immediate benefits of: collaborative filtering; real-time behavioral prediction; up-selling and/or cross-sell selling; banner advertisement income; Customer Relation Management (CRM); one-to-one banner management; site analysis reporting server; network-wide sales and marketing reports; product content and online pricing spidering; site management, backend product management, backend order processing, RMA processing, secured payment system, customer contact manager, price-search engine utility, and consolidated participation purchasing.

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

A computer implemented method and system for managing advertisements, electronic commerce, and consumer services are provided. An advertisement management platform, provided for managing an advertisement distribution service, registers one or more consumers with the advertisement distribution service by creating a consumer profile comprising dynamic behavioral data for each of the consumers. The advertisement management platform targets advertisements to the registered consumers based on their dynamic behavioral data. The advertisement management platform engages the registered consumers with the targeted advertisements based on an advertisement engagement plan of the advertisement distribution service. The advertisement management platform provides the registered consumers with one or more incentives when a response to the targeted advertisements confirms consumption of the targeted advertisements. An electronic commerce management server is provided for managing the electronic commerce and consumer services. A mobile wallet application is provided to each of the registered consumers for electronically storing the incentives.

Systems and methods for creating an ad hoc pervasive computing environment comprised of an inference recommendation engine coupled to commodity devices and sensors that passively collect human activity and behavioral data. Methods include machine learning and deep learning applications that analyze data to generate preference based recommendations to assist, inform, and guide subjects interacting with a connected living space and their connected social network.

The invention relates to a method for carrying out personalized recommendation on commodities by fusing a knowledge graph. The invention relates to the technical field of machine learning for personalized recommendation of commodities, and the method comprises the steps: obtaining the historical behavior data of a user, and generating a commodity knowledge graph; fusing the historical behavior data of the user with the commodity knowledge graph to construct a collaborative knowledge graph; adopting a representation learning method in machine learning to obtain vector embedding representation of nodes and relationships in the collaborative knowledge graph; embedding vectors of nodes and relations in the collaborative knowledge graph and inputting historical behavior data of the user into agraph convolutional neural network model based on an attention mechanism to form vector embedding representation of new nodes and relations containing neighborhood information; and defining a score function, calculating the probability that the user likes the commodity through the user and commodity vector embedding representation of the fused neighborhood information output by the graph convolutional neural network model, and performing sorting according to a prediction result to obtain a recommendation list.

The invention discloses an internet behavior markup engine and a behavior markup method corresponding to the same, and belongs to the technical field of user internet behavior data collection and analysis. The markup engine comprises a classification system module, a word segmentation base module, a semantic analysis module, a crawling program module, a rule base module, a knowledge base module, a rule parser module and a self-learning program module. The internet behavior markup method provides a basic logical structure that user behavior=behavior agent + behavior identification + behavior state. By the engine and the method, classification efficiency and accuracy are improved, description particle size of internet user behavior data is thinned, action, object and environmental conditions of one-time user behaviors are integrally recognized, and internet user behaviors are restored integrally. User behavior data outputted according to IUBML (internet universal behavior markup language) rules directly provide accurate advertising services based on user behaviors and demand understanding, and marketing requirements of corporate clients are met.

The invention provides an intelligent social advertisement release platform based on portrait data of big data. Real-time and offline behavior data of a user can be collected by a single interface (based on a dataflow model); integration and calculation are performed through a big data component; a calculation result is user portrait data; and a data organization form is a wide-table taking tags as fields. The portrait data is organized in a reverse index mode; and indexes of the data are stored in a big data warehouse. A Web platform takes multiple specific tags as input conditions and outputs statistical and aggregation results. During use, data specifically used for interests, hobbies and the like is analyzed, and advertisement release is performed according to specific hobbies of the user, so that the intelligent social advertisement release platform is formed, the advertisement release randomness and blindness are reduced, and the pertinence is achieved.

A method of analyzing vehicle data includes collecting behavior data of one or more surrounding vehicles with aid of one or more sensors on-board a sensing vehicle and analyzing the behavior data of the one or more surrounding vehicles with aid of one or more processors to determine a safe driving index for each of the one or more surrounding vehicles.

A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

A data fusion based video program recommendation method comprises the following steps: 1) collection of behavioral data of users: collecting the behavioral data of the users in browsing video websites and storing the behavioral data in a database; 2) data fusion: converting the behavioral data of a user in browsing a video program into the interest value of the user in the video program; and 3) program recommendation: according to the interest value of the user in a few programs, recommending the programs in which the user has high interest value to other users having the same favorites, wherein the behavioral data in browsing comprise behavioral data in playing. The duration ratio r is obtained by dividing the duration of the programs played by the users by the total duration of the programs. The higher the duration ratio r is, the higher the interest value is. As long as the users play the programs and other behavioral data of the users related to the interest value are fused, the program lists to be recommended can be generated more accurately and the degree of satisfaction of the users with the recommended programs is greatly improved.

The invention relates to a user behavior data collecting method and system. The user behavior data collecting method comprises the following steps of (a) receiving a request of accessing an application, which is sent by a client; (b) calling a pre-customized user behavior data rule; (c) generating user behavior insertion data according to the called user behavior data rule; (d) inserting the user behavior insertion data into the application, so as to generate an application page; (e) obtaining user behavior data according to a click event of the application page by a user. The user behavior data collecting method and system provided by the invention have stronger universality; the repeating work of user behavior data configuration can be reduced; the collected user behavior data rule can be sensitively customized, modified or deleted; the different types of user behavior collection data can be managed in a concentrated manner.

Embodiments of the present invention provide a communication fraud identification method and system. The method includes: obtaining a real-time bill, where the real-time bill includes at least a calling number and a called number; if it is determined that the calling number meets a preset condition, obtaining user behavior data related to the called number within a preset time, where the user behavior data includes at least communication data, base station data, signaling data, and service data that are related to the called number; generating a user behavior vector of the called number according to the user behavior data; determining a judgment model corresponding to the user behavior vector in a preset judgment model pool according to the user behavior vector; and determining whether a call corresponding to the real-time bill is a fraudulent call according to the judgment model. In embodiments of the present invention, the real-time bill is identified by using the user behavior datarelated to the called number within the preset time, so that identification of the fraudulent number that appears for the first time or only once is implemented, and time validity of communication fraud identification is improved.