Abstract: Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.

Abstract: Product demand forecasting accuracy utilizes partitional clustering of time series data with dynamic time warping. The product demand forecasting disclosed herein is particularly suited to forecasting product demand for products with limited sales data. Time-series sales data of a producs (or group of products) with limited sales data (e.g. a sparse or no time series of sales data) are dynamically time warped with sales data of products, or groups of products, having extensive sales data (e.g., an extensive time series of sales data) to determine a clustering model with an optimal number of clusters and a prototype time series for each cluster in the model. The prototype time series for the cluster in which the product (or group of products) with limited sales data lies is utilized as its product demand forecast.

Abstract: A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

Abstract: Embodiments of the present invention are directed to facilitating performing online data decomposition. In accordance with aspects of the present disclosure, an incoming data point of a time series data set is obtained. Thereafter, an iterative process of estimating trend and seasonality is performed to decompose the incoming data point to a set of data components based on a particular set of previous data points of the time series data set and corresponding data components. Generally, the set of data components for the incoming data point include a trend component, a seasonality component, and a residual component. The set of data components is provided for analysis of the incoming data point, such as, for example, to identify data anomalies.

Abstract: The present disclosure provides systems, methods, and metrics that filter out online visitor behavioral data that represents a potential lead with a high likelihood to convert to a vehicle sale from online visitor behavioral data that does not represent a potential lead with a low likelihood to convert to a vehicle sale, based on a mapping of sales back to observed website and vehicle configurator data. This enables more effective lead generation and the more efficient targeting of online incentive offers and sales “nudges,” for example. Further, the present disclosure enables web analytics data to be combined with sales data for sales forecasting in general.

Abstract: The subject technology identifies a series of journey event types in an online user journey, the event types including an impression event, an email event, a click event, and a website visit, and assigns an encoder to each event type. Using an assigned encoder, the technology encodes each event type to generate an encoded vector for each event type. The encoded vector is representative of at least a portion of the online user journey relating to that event type. The technology generates an encoded vector for each event type to create a set of encoded vectors, the set of encoded vectors including one or more of an impression event encoded vector, an email event encoded vector, a click event encoded vector, and a website visit encoded vector.

Abstract: A system and method for automated order preparation and fulfillment timing. The system is a cloud-based network containing an optimization server, portals for restaurants, customers, and drivers to enter their information, and an optimization engine which determines an optimal state for consumption of, and timing for fulfillment of, an order based on a multitude of variables associated with the business enterprises and delivery driver availability. The system may be accessed through web browsers or purpose-built computer and mobile phone applications.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for a geographic recommendation platform. The geographic recommendation platform receives data identifying a geographic region specified by a user and gathers data relating to the geographic region. The geographic recommendation platform determines, based on the data relating to the geographic region, an anticipated demand for an item within geographic region. The anticipated demand indicates how likely the item is to be purchased by a user that is located within the geographic region. The geographic recommendation platform generates a recommendation for the item based on the anticipated demand. The recommendation indicates the anticipated demand for the item within the geographic region. The geographic recommendation platform transmits the recommendation to the user.

Abstract: Methods, apparatuses, and computer program products are described herein that are configured to be embodied as a benchmarking service. In an example, an apparatus is configured to access input data, wherein the input data is representative of a current project; parse the input data to generate one or more input project units; extract one or more features from the one or more input project units, wherein the features are representative of at least one of project statistics, project bugs, project releases, project documentations, and organization data; receive a benchmarking model, wherein the benchmarking model was derived using a historical data set; and generate an output based on the benchmarking model and the one or more features, wherein the output is configured to provide an evaluation of the current project in the form at least one of a score and one or more recommendations.

Abstract: An Artificial Intelligence (AI)-based attribute prediction system generates predictions for attributes of highly customized equipment in response to received user requests. Processed historical data is initially used to generate feature combinations which are then employed along with a plurality of statistical and machine learning (ML) models in order to identify a best scoring model-feature combination in two selection cycles using multiple selection criteria. The predictions for an attribute are generated by the best scoring model and feature combination. Various insights regarding the features affecting the attribute can be additionally derived to provide recommendations to the user.

Abstract: A method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label; performing the classifier using customer feature data during a third period of time to obtain customers having positive predicted classifier labels; and performing the regressor using the customer feature data during the third period of time for the customers having positive predicted classifier labels, to obtain CLVs of the customers.

Abstract: Various embodiments described herein relate to techniques for forecasting with state transitions and confidence factors. In this regard, a system is configured to segment data associated with one or more assets to determine a set of classifications for one or more attributes related to the one or more assets. The system is also configured to generate a state machine associated with a Markov chain model based on the set of classifications for the data. Furthermore, the system is configured to perform a machine learning process associated with the state machine to determine one or more behavior changes associated with the one or more attributes related to the one or more assets. The system is also configured to predict, based on the one or more behavior changes associated with the one or more attributes related to the one or more assets, a change in demand data for the one or more assets during a future interval of time.

Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

Abstract: Techniques are described herein for providing adaptable testing and benchmarking of user experiences with respect to one or more products. In some embodiments, the techniques include systems and methods for predicting performance of facets of a user experience under new testing and benchmarking methodologies. The systems and methods may generate a prediction for the results of a UX test even though the methodology and mechanics to quantify the user experience may vary significantly from previous methodologies. The techniques allow for methodologies to evolve over time without losing historical context or the ability to meaningfully compare historical test results with tests run using updated testing and benchmark models. Further, the techniques allow for benchmarks to be computed in real-time or near real-time as methodologies change without requiring tests to be run according to the new methodologies.

Abstract: Digital content interaction prediction and training techniques that address imbalanced classes are described. In one or more implementations, a digital medium environment is described to predict user interaction with digital content that addresses an imbalance of numbers included in first and second classes in training data used to train a model using machine learning. The training data is received that describes the first class and the second class. A model is trained using machine learning. The training includes sampling the training data to include at least one subset of the training data from the first class and at least one subset of the training data from the second class. Iterative selections are made of a batch from the sampled training data. The iteratively selected batches are iteratively processed by a classifier implemented using machine learning to train the model.

Abstract: Techniques are described for generating seasonal forecasts. According to an embodiment, a set of time-series data is associated with one or more classes, which may include a first class that represent a dense pattern that repeats over multiple instances of a season in the set of time-series data and a second class that represent another pattern that repeats over multiple instances of the season in the set of time-series data. A particular class of data is associated with at least two sub-classes of data, where a first sub-class represents high data points from the first class, and a second sub-class represents another set of data points from the first class. A trend rate is determined for a particular sub-class. Based at least in part on the trend rate, a forecast is generated.

Abstract: Embodiments provide techniques for autonomous vehicle fleet modeling and simulation, such as within a dynamic transportation matching system utilizing one or more vehicle types such as non-autonomous vehicles and autonomous vehicles. An autonomous fleet simulation model may be generated based on real-world parameters of an autonomous vehicle fleet, and the parameters may be modified in a simulation in order to determine optimized values that may be applied to the real-world autonomous vehicle fleet.

Abstract: This disclosure relates to a system and method to estimate demand transfer of a product while considering performance of all the products of a category simultaneously. It would be appreciated that the demand of a removed product transfers to other products of same category in a store. In addition the demand transfer is influenced by sales drivers such as product level promotion and competitor prices, store location, weather and seasonality. By considering these factors the proposed approach provides a method to estimate demand transfer of a product. It is addressed by creating multivariate multi structure machine learning models and estimating demand transfer values by using suitable scenario generator for product availability. It enables to estimate more holistic demand transfer values by simultaneous consideration of individual product behaviours with respect to other products availability and other sales drivers.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification.

Abstract: Techniques for using online engagement footprints for video engagement prediction are provided. In one technique, events are received from multiple client devices, each event indicating a type of engagement of a video item from among multiple types of engagement. One or more machine learning techniques are used to train a prediction model that is based on the events and multiple features that includes the multiple types of engagement. In response to receiving a content request, multiple entity feature values are identified for a particular entity that is associated with the content request. Two or more of the entity feature values correspond to two or more of the types of engagement. A prediction is generated based on the entity feature values and the prediction model. The prediction is used to determine whether to select, from candidate content items, a particular content item that includes particular video.

Abstract: Item delivery optimization may be provided. Items may be identified as available for delivery at a first location to at least one second location. A route from the first location to the second location may be calculated and at least a portion of the route from the first location to the second location may be provided to a delivery mechanism, such as an automated delivery device and/or a user.

Abstract: An example method of complementary consumer item selection includes: receiving, by a computer system, an image representing a first consumer item of a reference set of consumer items; identifying a target category of a complementary consumer item to be associated with the reference set of consumer items; generating, by a neural network processing the set of images, a feature embeddings representing the first consumer item in relation to the target category; selecting, using the feature embedding, from a set of available consumer items, a plurality of candidate consumer items associated with the target category; and selecting, among the plurality of candidate consumer items, the complementary consumer item to be associated with the reference set of consumer items.

Abstract: System and method to build and score predictive model for numerical attributes are provided. The system includes a memory and a processing subsystem. The processing subsystem is configured to select one or more numerical variables from the plurality of data sets based on a plurality of parameters, to apply feature engineering and transformation on the one or more numerical variables, to perform time series forecasting on the one or more numerical variables based on the plurality of features extracted, to evaluate and select appropriate prediction technique based a regression technique based on a plurality of elements, to build a prediction model, to score the built prediction model based on the performed time series forecasting and an evaluated regression technique and to predict the built prediction model based on an obtained score. Further, the system uses the plurality of parameters and the prediction method to score and predict the prediction model.

Abstract: Systems and methods are provided herein for producing and vending ice in an energy-efficient manner. A system for producing and vending ice comprises: an ice-making device configured to manufacture ice; a reservoir coupled to the ice-making device and configured to store the manufactured ice manufactured; and a control circuit communicatively coupled to the ice-making device, the control circuit configured to: determine a beginning ice inventory at a first time; determine a predicted ice demand for a first period, wherein the predicted ice demand is a function of at least a historical factor, a weather factor, a customer social event factor, and a public social event factor; determine an ice manufacturing quantity based on the beginning ice inventory, the predicted ice demand, and a manufacturing capacity of the ice-making device; and cause the ice-making device to produce an amount of ice consistent with the determined ice manufacturing quantity.

Abstract: This disclosure describes one or more implementations of a model segmentation system that generates accurate audience segments for client devices/individuals utilizing multi-class decision tree machine-learning models. For example, in various implementations, the model segmentation system generates a customized loss penalty matrix from multiple loss penalty matrices. In particular, the model segmentation system can generate regression mappings of model evaluation metrics for a plurality of decision tree models and combine loss penalty matrices based on the regression mappings to generate a customized loss penalty matrix that best fits an administrator's customized needs of segment accuracy and reach. The model segmentation system then utilizes the customized loss penalty matrix to train a multi-class decision tree machine-learning model to classify client devices into non-overlapping audience segments.

Abstract: An automatic system facilitates selection of media properties on which to display an advertisement, responsive to a profile collected on a first media property, where a behavioral-targeting company calculates expected profit for an ad correlated with the profile and arranges for the visitor to be tagged with a tag readable by the selected media property. The profit can be calculated by deducting, from the revenues that are expected to be generated from an ad delivered based on the collected profile, at least the price of ad space at a media property where the BT company might like to deliver ads to the profiled visitor. When the calculated profit is positive (i.e., not a loss), the BT company arranges for the visitor to be tagged with a tag readable by the selected media property through which the BT company expects to profit.

Abstract: A processing device may analyze a set of time series data using a time series forecasting model comprising an attributes model and a trend detection model. The attributes model may comprise a modified gradient boosting decision tree (GBDT) based algorithm. Analyzing the set of time series data comprises determining a set of features of the set of time series data, the set of features including periodic components as well as arbitrary components. A trend of the set of time series data may be determined using the trend detection model and the set of features and the trend may be combined to generate a time series forecast.

Abstract: Embodiments generate a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set.

Abstract: A system and method for en-route business selection, routing, and order preparation timing. The system is a cloud-based network containing an optimization server, portals for restaurants, customers, and drivers to enter their information, and an optimization engine which determines optimal pickup and delivery times for delivery drivers based on a multitude of variables associated with the business enterprises and delivery driver availability. The system may be accessed through web browsers or purpose-built computer and mobile phone applications.

Abstract: Methods and apparatus to estimate audience sizes using deduplication based on vector of counts sketch data are disclosed. An example apparatus to determine an audience size for media based on vector of counts sketch data includes: a coefficient analyzer to determine coefficient values of a polynomial based on variances, a covariance, and cardinalities corresponding to a first vector of counts from a first database and a second vector of counts from a second database; an overlap analyzer to determine a real root of the polynomial, the real root corresponding to an estimate of an overlap between the first vector of counts and the second vector of counts; and a report generator to estimate the audience size based on the estimate of the overlap and the cardinalities of the first vector of counts and the second vector of counts.

Abstract: Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

Abstract: In various embodiments, the disclosed system may be utilized in managing supply and demand chains to optimize creation and management of inventory buffer levels via machine learning. In several embodiments, optimizing inventory buffer levels may lead to minimizing costs while maximizing the availability and timeliness of product deliveries. In multiple embodiments, the disclosed system may utilize supply chain data and performance monitoring with automated machine learning and derivation of demand signatures and profiles to provide optimized creation and management of inventory buffer levels.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: Example user behavior prediction methods and apparatus are described. One example method includes obtaining a first contribution value of each piece of characteristic data for a specified behavior after obtaining behavior prediction information including a plurality of pieces of characteristic data. Every N pieces of characteristic data in the plurality of pieces of characteristic data may be processed by using one corresponding characteristic interaction model, to obtain a second contribution value of the every N pieces of characteristic data for the specified behavior. Finally, an execution probability of executing the specified behavior by a user may be determined based on the obtained first contribution value and the obtained second contribution value, to predict a user behavior. In the example method, interaction impact of the plurality of pieces of characteristic data on the specified behavior is considered during behavior prediction.

Abstract: The present disclosure describes a computer-implemented method that includes: accessing data encoding historical records involving a plurality of materials within an enterprise network, the historical records indicating a consumption pattern of each material as well as a delivery pattern of each material; based on the consumption pattern, identifying one or more hubs to stock the plurality of materials for multiple customer plants in one or more regions of an enterprise network; based on the consumption pattern of each material, determining a quantity level for stocking the material at the one or more hubs to respond to a demand for the material from the one or more of the customer plants in the one or more regions; and based on the delivery pattern of each material, monitoring an estimated lead time to respond to the demand for the material being stocked at the one or more hubs and at the quantity level.

Abstract: Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for determining at least one characteristic about a figure and searching a data set based on an indicated search area for at least one entry that falls within a threshold value of the determined at least one characteristic about the figure, wherein the search area indicates which part of the data set to be searched. Displaying the at least one entry from the data set that falls within a threshold value of the determined at least one characteristic about the figure.

Abstract: Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.

Abstract: A computer-implemented method can receive a new plan deviation alert having a deviation level that quantifies a mismatch between expected supply chain parameters specified by a supply chain plan and observed supply chain parameters. Responsive to the new plan deviation alert, the method can perform a rule-based search to find a plurality of potential remediation solutions to correct the mismatch. The method can simulate implementation of the potential remediation solutions and evaluate expended resources associated with them. Based on the evaluated expended resources, the method can generate a ranked list of candidate remediation solutions and display the ranked list of candidate remediation solutions in a user interface. The method can receive a selected remediation solution from the ranked list of candidate remediation solutions for initiation. Machine learning can be used on an expert user's selection to adapt to the expert's preferences and provide more relevant remediation solutions.

Abstract: Methods and systems for publishing a playlist are disclosed. A user generates or selects a playlist, which is then provided (e.g., uploaded) for publishing. A playlist identifying at least one of one or more tracks and one or more albums is received. The playlist may then be published such that the playlist is viewable by one or more individuals. A user may then purchase one or more tracks/albums identified in the playlist via an online store.

Abstract: Certain aspects involve providing automated performance monitoring of statistical models. For example, a processing device is used for performing a statistical analysis on information in an archive to extract historical data, scores, and attributes. The processing device calculates performance metrics based at least in part on the historical data, scores, and attributes. The processing device pre-calculates summary performance data based at least in part on the performance metrics. The summary performance data is stored in files with predefined layouts, which are stored in a non-transitory, computer-readable medium. Segmented data is presented from a file to a user through a graphical user interface (GUI). In some aspects, various reports of the segmented data are presented interactively by detecting a selection by the user of a segmentation and displaying the corresponding segmented data.

Abstract: Methods, systems, and computer-readable storage media for tuning behavior of a machine learning (ML) model by providing an alternative loss function used during training of a ML model, the alternative loss function enhancing reliability of the ML model, calibrating the confidence of the ML model after training, and reducing risk in downstream tasks by providing a mapping between the confidence of the ML model to the expected accuracy of the ML model.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of individual time series associated with a set of users. Each of the individual time series can be associated with a respective user out of the set of the users. A plurality of variables represented via the set of individual time series can be selected. The plurality of variables can include at least a first variable and a second variable. One or more regression techniques can be applied to at least the first variable and the second variable. A set of sensitivity metrics for the set of users can be determined based on the one or more regression techniques. A respective sensitivity metric out of the set of sensitivity metrics can be determined for each of the users.

Abstract: This disclosure describes one or more implementations of a model segmentation system that generates accurate audience segments for client devices/individuals utilizing multi-class decision tree machine-learning models. For example, in various implementations, the model segmentation system generates a customized loss penalty matrix from multiple loss penalty matrices. In particular, the model segmentation system can generate regression mappings of model evaluation metrics for a plurality of decision tree models and combine loss penalty matrices based on the regression mappings to generate a customized loss penalty matrix that best fits an administrator's customized needs of segment accuracy and reach. The model segmentation system then utilizes the customized loss penalty matrix to train a multi-class decision tree machine-learning model to classify client devices into non-overlapping audience segments.

Abstract: A wireless power system for powering a television includes a source resonator, configured to generate an oscillating magnetic field, and at least one television component attached to at least one device resonator, wherein the at least one device resonator is configured to wirelessly receive power from the source resonator via the oscillating magnetic field when the distance between the source resonator and the at least one device resonator is more than 5 cm, and wherein at least one television component draws at least 10 Watts of power.

Abstract: Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features.

Abstract: A method, apparatus and computer program product are provided for providing auto-replenishment of an inventory of promotions. Auto-replenishment amounts and dates may be determined based on a variety of factors including current inventory, outstanding promotions, redemption ratios and redemption rates. Redemption ratios and redemption rates may be characterized based on provider information, consumer information, and/or promotion information. Auto-replenishment may result in a smoothed redemption curve and optimal impact to the provider.

Abstract: There is provided an apparatus and method for a webshop such that the products shown to a customer are related to the purchasing habits of the customer. A control unit is arranged to communicate with a product information database, a product category database and a customer purchase history database. The control unit includes a product categorising unit arranged to generate at least one product category based on product information in the product information database and to store the at least one generated product category in the product category database. A calculating unit is arranged to calculate a probability of a customer being an underbuyer/overbuyer of a type of product based on the customer's purchase history stored in the customer purchase history database and the at least one product category from the product category database.

Abstract: Behavior data associated with a user is obtained. The behavior data is generated when the user uses an Internet service and includes a user identification and identification information indicating the Internet service. At least one predefined carbon-saving quantity quantization algorithm is determined based on the identification information related to the Internet service. A carbon-saving quantity associated with the user is calculated based on the obtained behavior data and the determined at least one predefined carbon-saving quantity quantization algorithm. Based on the calculated carbon-saving quantity associated with the user and the user identification, user data is processed. The user data is related to the carbon-saving quantity associated with the user.