Abstract: The present invention provides a text classification backdoor attack method, system, device and a computer storage medium. The method includes: training a pretraining model by using a clean training set to obtain a clean model; generating a pseudo label data set by using a positioning label generator; performing multi-task training on a Sequence-to-Sequence model by using the pseudo label data set to obtain a locator model; generating a backdoor data set by using the locator model; and training the clean model by using the backdoor data set to obtain a dirty model. A pseudo label data set is generated by using a pretrained clean model without manual annotation. A backdoor attack location in a text sequence may be dynamically predicted by using a locator model based on a Sequence-to-Sequence and multi-task learning architecture without manual intervention, and a performance indicator obtained by dynamically selecting an attack location is better.

Abstract: A method for training a polyhedral classifier is described including obtaining training data in a data space, the training data including first data points associated with a first label and second data points associated with a second label, determining a pair of hyperplanes by determining an orientation of the pair of hyperplanes based on a minimization of a distance between the pair of hyperplanes such that the first data points lie between the hyperplanes in relation to a distance between the pair of hyperplanes such that both the first data points and the second data points lie between the hyperplanes and determining the position of the pair of hyperplanes such that the first data points lie between the pair of hyperplanes and the second data points are at least partially separated from the first data points by the pair of hyperplanes.

Abstract: Provided are a computer-implemented method, a system, and a computer program product. The method comprises extracting features from a plurality of base models in an ensemble model. The plurality of base models are configured to provide respective prediction results. The ensemble model is configured to provide an overall prediction result from the prediction results of the plurality of base models. The features are associated with time performance of the base models. The method further comprises clustering the plurality of base models into a plurality of clusters based on the extracted features. The method further comprises assigning the plurality of base models to a plurality of parallel computation units based on the plurality of clusters.

Abstract: Systems and methods for query-based recommendation systems using machine learning-trained classifiers are provided. A service provider server receives, from a communication device through an application programming interface, a query in an interaction between the server provider server and the communication device. The service provider server generates a vector of first latent features from a set of first visible features associated with the query using a machine learning-trained classifier. The service provider server generates a likelihood scalar value indicating a likelihood of the query is answered by a candidate user in a set of users using a combination of the vector of first latent features and a vector of second latent features. The service provider server provides, to the communication device through the application programming interface, a recommendation message as a response to the query, where the recommendation message includes the likelihood scalar value and an indication of the candidate user.

Abstract: Methods, systems and computer program products for generating content for training a classifier, including: receiving two or more parts of a description, for each part, retrieving from an extracted feature collection library one or more extracted feature collections derived from one or more video frames, the extracted feature collections or the video frames labeled with a label associated with the part, thus obtaining a multiplicity of extracted feature collections, and combining the multiplicity of extracted feature collections to obtain a combined feature collection associated with the description, the combined feature collection to be used for training a classifier.

Abstract: One embodiment of the present invention sets forth a technique for predicting fraud by correlating user behavior biometric data with one or more other types of data. The technique includes receiving cursor movement data generated via a client device and analyzing the cursor movement data based on a model to generate a result. The model may be generated based on cursor movement data associated with a first group of one or more users. The technique further includes receiving log data generated via the client device and determining, based on the result and the log data, that a user of the client device is not a member of the first group.

Abstract: There is provided a solution for employing a neural network in a radio receiver. According to an aspect, a method comprises: acquiring, from a received frame, a set of samples associated with a position of a control information element in the frame; inputting the set of samples to nodes of an input layer of a neural network, the input layer having the same number of nodes as a number of samples in the set of samples; processing the set of samples in the neural network that has been trained, before receiving the set of samples, to decode one or more determined values of the control information element and to detect discontinuous transmission; outputting, in an output layer of the neural network, an indicator indicating a decoded value of the control information element comprised in the set of samples or an indicator indicating the discontinuous transmission.

Abstract: Provided herein is an integrated circuit including, in some embodiments, a special-purpose host processor, a neuromorphic co-processor, and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The special-purpose host processor can be operable as a stand-alone processor. The neuromorphic co-processor may include an artificial neural network. The co-processor is configured to enhance special-purpose processing of the host processor through an artificial neural network. In such embodiments, the host processor is a pattern identifier processor configured to transmit one or more detected patterns to the co-processor over a communications interface. The co-processor is configured to transmit the recognized patterns to the host processor.

Abstract: A computer-implemented method of training a machine learnable function, such as an image classifier or image feature extractor. When applying such machine learnable functions in autonomous driving and similar application areas, generalizability may be important. To improve generalizability, the machine learnable function is rewarded for responding predictably at a layer of the machine learnable function to a set of differences between input observations. This is done by means of a regularization objective included in the objective function used to train the machine learnable function. The regularization objective rewards a mutual statistical dependence between representations of input observations at the given layer, given a difference label indicating a difference between the input observations.

Abstract: Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

Abstract: The present disclosure relates to systems and methods for enhanced mapping of transaction data to a target document, and for classifying line items of the mapped transaction data, using classification algorithms. Embodiments provide a system including a column mapping module to receive a target scheme specifying a target structure for the target document, receive transaction data having a source structure, and map at least one source column to at least one target column of the target columns based on application of classification algorithms to features identified from the source transaction data. The system also includes a row classification module to classify the rows of the mapped transaction data into classification categories. The system also includes a validation handler to receive validation input from a user, validating the column mapping and/or the row classification. The validating including accepting the recommendation or rejecting the recommendation and selecting a correct choice.

Abstract: The subject disclosure relates to techniques for improving map labeling and in particular, for automating process of semantically labeling maps used for autonomous vehicle (AV) navigation. In some aspects, a process of the disclosed technology includes steps for receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map, processing the 3D LiDAR map to produce a 2D map, rasterizing the 2D map into a plurality of map tiles, and providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles. Systems and machine-readable media are also provided.

Abstract: The present disclosure is directed to methods and systems for knowledge distillation.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of machine learning selection and/or application of a data model defined in a spreadsheet. In one embodiment, a method of spreadsheet data analysis utilizing machine learning includes processing a spreadsheet file comprising a formula algorithm to be applied to a dataset, including spreadsheet formulas stored in a first set of one or more cells. Generating from the formula algorithm may be an extrapolated algorithm, expressed in a programming language. The method runs an automatic machine learning process to automatically apply one or more predictive models to the dataset, determines a predictive model of the one or more predictive models fits the dataset, and modifies the extrapolated algorithm in response to an application of the one or more predictive models to the dataset to result in a modified extrapolated algorithm.

Abstract: A memory apparatus embedded with a computing function and an operation method thereof are provided. The memory apparatus includes a memory array, a plurality of data flow controllers, a plurality of computation circuits, a data bus, and a control logic circuit. The memory array includes a plurality of block groups having a plurality of memory blocks. Each of the data flow controllers selects a transmission path of data of each memory block according to a corresponding one of the data flow control signals. In a computation mode, the computation circuit computes first data from the corresponding memory block. In a normal mode, second data is transmitted between the data bus and the corresponding memory block. The data flow controller transmits the first data from the corresponding memory block to the computation circuit according to the data flow control signal provided by the control logic circuit to compute the first data.

Abstract: Pattern classification systems and methods are disclosed. The pattern classification systems and methods employ one or more classification networks that can parse multiple patterns simultaneously while providing a continuous feedback about its progress. Pre-synaptic inhibition is employed to inhibit feedback connections to permit more flexible processing. Various additional improvements result in highly robust pattern recognition systems and methods that are suitable for use in research, development, and production.

Abstract: A method of product update analysis and management includes receiving metadata of a product update related to a code change of an application on an endpoint of a managed network. The method includes scraping posts related to the product update from two different internet websites. The method includes aggregating the posts from the internet websites. The method includes quantifying a social volume from the aggregated posts. The social volume being a measure of discussion related to the product update. The method includes extracting content from the aggregated posts. Based on the extracted content, the method includes summarizing the posts into a collection of terms or phrases representative of a topic of the posts. The method includes causing display of an indication of the social volume and the collection of terms or phrases. The method includes implementing the product update to affect a change in program code at the application.

Abstract: Disclosed is a method for determining open loop power control parameters. A first set of key performance indicators associated with a first set of open loop power control parameters is obtained from one or more first base stations. A statistical model is determined based at least partly on the first set of key performance indicators and the first set of open loop power control parameters. A second set of open loop power control parameters is determined based at least partly on the statistical model.

Abstract: A method and system provide the ability to generate and use synthetic data to extract elements from a floor plan drawing. A room layout is generated. Room descriptions are used to generate and place synthetic instances of symbol elements in each room. A floor plan drawing is obtained and pre-processed to determine a drawing area. Based on the synthetic data symbols in the floor plan drawing are detected. Orientations of the detected symbols are also detected. Based on the detected symbols and orientations, building information model (BIM) elements are fetched and placed in the floor plan drawing.

Abstract: Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.

Abstract: Machine learning-based approaches are used to detect trends of behavior and anomalous events from customer support messages between customers and customer support agents or other appropriate resources in an electronic environment. For example, for a plurality of time periods, a prediction model can be trained. The prediction models can be trained on messages that correspond to each prediction models' period of time. The prediction models can process messages to determine a score (e.g., a representative confidence score) for the time period a prediction model is associated with. For a selected time period, a model (e.g., a trend detection model) can be applied to the scores for time periods before the selected time period to determine whether the score for the selected time period is associated with an anomalous event. Thereafter, an alert can be presented with, for example, the messages that triggered the alert, among other such information.

Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.

Abstract: A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior for a plurality of users into a set of models that represent user actions, and the optimal actions, given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on a best in class model derived from the best performing user. The models are created using the DensiCube, random forest, K-means or other machine learning algorithms.

Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.

Abstract: Disclosed is a beamforming method using a deep neural network. The deep neural network may include an input layer, L hidden layers, and an output layer, and the beamforming method may include: obtaining channel information h between a base station and K terminals and a transmit power limit value P of the base station, and inputting h and P into the input layer; and performing beamforming on signals to be transmitted to the K terminals using beamforming vectors derived using the output layer and at least one activation function, wherein the base station transmits the signals to the K terminals using M transmit antennas. Here, the output layer may be configured in a direct beamforming learning (DBL) scheme, a feature learning (FL) scheme, or a simplified feature learning (SFL) scheme.

Abstract: A computing system for detecting a pattern of fraudulent network events in a payment card network is configured to continuously receive a plurality of scored transaction authorization requests each including a respective account number and a respective fraud score. The computing system is also configured to sort the scored transaction authorization requests into account ranges based the account number, and sort the transaction authorization requests within each of the account ranges into a fraud score range stripes based on the corresponding fraud score. The computing system is further configured to calculate, for the scored transaction authorization requests within each fraud score range stripe, a ratio of a cumulative metric for a shorter time period over a longer time period, and detect, in near real-time, a fraud event associated with one of the account ranges based on the ratio for one of the fraud score range stripes within the account range.

Abstract: A student neural network may be trained by a computer-implemented method, including: inputting common input data to each teacher neural network among a plurality of teacher neural networks to obtain a soft label output among a plurality of soft label outputs from each teacher neural network among the plurality of teacher neural networks, and training a student neural network with the input data and the plurality of soft label outputs.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate quantum topological classification are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure. The computer executable components can further comprise a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.

Abstract: Embodiments of the present disclosure provide a method and apparatus for processing an image, and relates to the field of computer vision technology. The method may include: acquiring a value to be processed, where the value to be processed is associated with an image to be processed; and processing the value to be processed by using a quality scoring model to generate a score of the image to be processed in a target scoring domain, where the score of the image to be processed in the target scoring domain is related to an image quality of the image to be processed.

Abstract: A method for using distributed representations of data items within a first set of data documents clustered in a first two-dimensional metric space to generate a cluster of distributed representations in a second two-dimensional metric space includes clustering in a first two-dimensional metric space, by a reference map generator, a set of data documents, generating a semantic map. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator generates a distributed representation using occurrence information about each data item. A sparsifying module receives an identification of a maximum level of sparsity and reduces a total number of set bits within the distributed representation. The reference map generator clusters, in a second two-dimensional metric space, a set of SDRs retrieved from the SDR database and selected according to a second at least one criterion, generating a second semantic map.

Abstract: A service action category based cloud security system and method implement cloud security by categorizing service actions of cloud service providers into a set of service action categories. The service action categorization is performed agnostic to the applications or functions provided by the cloud service providers and also agnostic to the cloud service providers. With the service actions of cloud service providers thus categorized, cloud security monitoring and threat detection can be performed based on service action categories. Thus, cloud security can be implemented without requiring knowledge of the applications supported by the cloud service providers and without knowing all of the individual service actions supported by the cloud service providers.

Abstract: Systems and methods of determining file-access patterns in at least one computer network, the network comprising a file-access server, including training a first machine learning (ML) algorithm with a first training dataset comprising vectors representing network traffic such that the first ML algorithm learns to determine network characteristics associated with file-access traffic, determining, using the first ML algorithm, network characteristics based on highest interaction of traffic with the file-access server compared to other interactions in the at least one computer network, and determining file-access patterns in the at least one computer network based on the network characteristics associated with file-access traffic.

Abstract: A reference map generator clusters, into a semantic map, a set of data documents selected according to at least one criterion and associated with a medical diagnosis. A parser generates an enumeration of measurements occurring in the set of data documents. A representation generator generates for each measurement in the enumeration, a sparse distributed representation (SDR). The method includes storing, by a processor on a second computing device, in each of a plurality of memory cells on the second computing device, one of the generated SDRs. A diagnosis support module receives a document comprising a plurality of measurements. The representation generator generates a compound SDR for the document. Each of the plurality of bitwise comparison circuits determine a level of overlap between the compound SDR and the stored generated SDR. The diagnosis support module provides an identification of the medical diagnosis associated with a stored SDR.

Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

Abstract: A method and system is provided for identifying patterns in datasets by identifying delimited regions of feature-space in which patterns occur. The delimited regions are then combined into an ensemble able to make predictions based on the identified regions of feature-space. The method may be used for classification, for regression, for auto-encoding, for simulation, and for other applications of pattern detection.

Abstract: There is provided a system and a method of generating an annotated structured dataset, comprising: receiving a medical classification term, searching over the unstructured patient data for extracting unclassified unstructured text fragments, presenting a subset of the unclassified unstructured text fragments, receiving an indication of a selection of none or at least one of the text fragments, and one of: (i) classifying non-selected unclassified unstructured text fragments according to the medical classification term, and classifying selected text fragments as not satisfying the medical classification term, and (ii) classifying selected unclassified unstructured text fragments according to the medical classification term, and classifying non-selected unclassified unstructured text fragments as not satisfying the medical classification term, and iterating the searching, and/or the presenting, until no text fragments are obtained by the search, wherein the annotated structured dataset is created by the classif

Abstract: A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Abstract: In an embodiment, a list of domains is received that includes one or more categories for each domain. The categories are assigned to each domain using a classifier that is trained using features extracted from webpages known to be associated with particular categories. An administrator creates access rules for users, or groups of users, that control the categories of domains that each user is permitted to access or not access. When a user makes a request for a webpage, access rules associated with the user are retrieved, and one or more categories associated with the domain of the requested webpage are determined using the list of domains. If any of the one or more categories of the domain violate an access rule associated with the user, the request for the webpage is denied. Otherwise the user is allowed to access the webpage.

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are described for machine learning-based techniques for reducing the visual complexity of a dependency graph that is representative of an application or service. For example, the dependency graph is generated that comprises a plurality of nodes and edges. Each node represents a compute resource (e.g., a microservice) of the application or service. Each edge represents a dependency between nodes coupled thereto. A machine learning-based classification model analyzes each of the nodes to determine a likelihood that each of the nodes is a problematic compute resource. For instance, the classification model may output a score indicative of the likelihood that a particular compute resource is problematic. The nodes and/or edges having a score that exceed a predetermined threshold are provided focus via the dependency graph.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: Combining predictions made by multiple different prediction systems, including: obtaining a new input sample; obtaining a combiner which comprises optimal selection values that are configured to maximize a predefined performance measure; automatically applying multiple different prediction systems to the new input sample, to generate predictions; and automatically selectively combining the generated predictions based on the optimal selection values, to generate a combined prediction whose predefined performance measure is improved compared to individual usage of each of the prediction systems.

Abstract: The present disclosure provides a computation device and method. The device may include an input module configured to acquire input data; a model generation module configured to construct an offline model according to an input network structure and weight data; a neural network operation module configured to generate a computation instruction based on the offline model and cache the computation instruction, and compute the data to be processed based on the computation instruction to obtain a computation result; and an output module configured to output a computation result. The device and method may avoid the overhead caused by running an entire software architecture, which is a problem in a traditional method.

Abstract: A surgical instrument navigation system is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient. The surgical instrument navigation system includes: a surgical instrument; an imaging device which is operable to capture scan data representative of an internal region of interest within a given patient; a tracking subsystem that employs electro-magnetic sensing to capture in real-time position data indicative of the position of the surgical instrument; a data processor which is operable to render a volumetric, perspective image of the internal region of interest from a point of view of the surgical instrument; and a display which is operable to display the volumetric perspective image of the patient.

Abstract: A method that may include training a student ODNN to mimic a teacher ODNN. The training may include calculating a teacher student detection loss that is based on a pre-bounding-box output of the teacher ODNN. The pre-bounding-box output of the teacher ODNN is a function of pre-bounding-box outputs of different ODNNs that belong to the teacher ODNN. The method may also include detecting one or more objects in an image, by feeding the image to the trained student ODNN; outputting by the trained student ODNN a student pre-bounding-box output; and calculating one or more bounding boxes based on the student pre-bounding-box output.

Abstract: An image processing method includes a first step of acquiring input data including a captured image and optical system information relating to a state of an optical system used for capturing the captured image and a second step of inputting the input data to a machine learning model and of generating an estimated image acquired by sharpening the captured image or by reshaping blurs included in the captured image.

Abstract: Systems and methods for aggregating data. The system is configured to receive metadata from an interactive graphical user interface (GUI) of a user device, aggregate field values from the data stored on one or more databases based on the received metadata and generate filter instructions based on the received metadata. The system is further configured to transmit the aggregated field values and the filter instructions to the user device, receive a user-customized filter set and subscription request for a synthetic symbol associated with the user-customized filter set from the user device, and create the synthetic symbol responsive to the subscription request. Moreover, the system aggregates one or more data values from the data stored on the databases associated with the created synthetic symbol and generates instructions to display the data values on the interactive GUI in accordance with the user-customized filter set associated with the created synthetic symbol.

Abstract: Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.