Abstract: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

Abstract: A recommender system can include a defender computing device that is configured to obtain customer interaction data characterizing customer interactions with an ecommerce marketplace. The defender computing device can also be configured to determine an item recommendation based on the customer interaction data using a trained differentially private recommendation model and send the item recommendation to the customer. The trained differentially private recommendation model is more likely to determine the same item recommendation after poisoned data is injected into the customer interaction data than a recommendation model that is not privately trained.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: This application relates to apparatus and methods for automatically detecting attacks to advertisement systems. In some examples, a computing device trains a machine learning process based on a training dataset. The training dataset may be an identified portion of a website session dataset that includes a lower percentage of malicious data caused by attacks than other portions, or may include no malicious data. Once trained, the computing device generates features from a website session dataset for a customer, and applies the trained machine learning process to the generated features to detect malicious data within the website session dataset for the customer. Further, the computing device may filter the website session data to remove the detected malicious data, and may store the filtered website session data within a data repository. The computing device may provide the filtered website session data to a recommendation system to generate item recommendations for the customer.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving a user request via a graphical user interface, the user request corresponding to a user search query for a product; determining whether a first processing machine of the system is operating in a first processing mode or a second processing mode; when the first processing machine is determined to be operating in the first processing mode, analyzing the user request via the first processing machine and using a process, to identify a candidate recommendation system to utilize by: determining a randomized strategy for one or more candidate recommendation systems based on a ratio of a number of the one or more candidate recommender systems, the randomized strategy to be stored in a collected history data; determining model parameters based on the collected history data; and

Abstract: In some implementations, a provider edge device associated with a link aggregation group (LAG) may maintain, according to a link aggregation control protocol (LACP), a set of links that connect the PE device to a consumer edge device. The provider edge device may determine that the provider edge device and another provider edge device associated with the LAG are not receiving link aggregation control protocol data units (LACPDUs) from the consumer edge device. The provider edge device may cause the set of links to have a maintain LAG status, which causes the provider edge device to keep up the set of links and to cease maintaining the set of links according to the LACP. The provider edge device may route, based on causing the set of links to have the maintain LAG status, one or more packets to or from the consumer edge device via the set of links.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform operations comprising: identifying a segment of users who are value conscious about a product by: evaluating a number of activities of the users indicating whether or not the users are value conscious for the product; and generating, using a conditional probability equation, a probability that a user of the users will show interest in the product; and transmitting instructions to display, for viewing by the user, a recommendation for the product, wherein the recommendation is based on the probability being above a threshold that the user will show interest in the product. Other embodiments are disclosed herein.

Abstract: In some examples, a system can include at least one processor and a memory storing instructions. In various examples, the at least one processor executes the instructions to, during a chat session, obtain, from a computing device of a first user, session data associated with the chat session and a first user. Additionally, the at least one processor executes the instructions to obtain a first set of data identifying a set of price drop items associated with the chat session, and obtain a second set of data identifying a set of attribute values associated with one or more attribute features of each item type. Based on the first set of data and the second set of data, the at least one processor may execute the instructions to implement a first set of operations that generate a third set of data. In some examples, the third set of data including affinity value data for each item of the set of price drop items.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: A fulfillment intent system can include a computing device configured to receive an indication of an event occurring from a user device and obtain a set of historical data associated with a user identifier indicated by the user device. The computing device is further configured to determine a fulfillment parameter by applying a machine learning model to the set of historical data and obtain a set of item identifiers based on the indication. The computing device is also configured to organize the set of item identifiers based on the fulfillment parameter and transmit the set of item identifiers to the user device for display on a user interface of the user device.

Abstract: System and method for time-aware deep learning are provided. A training data set including continuous-time data and a deep learning architecture are received. A trained deep learning model is generated using a temporal kernel approach. The temporal kernel approach includes constructing a temporal kernel based on the continuous-time data and composing the temporal kernel with a selected hidden layer of the deep learning architecture to generate a hidden output. The trained deep learning model is output for use in one or more machine learning tasks.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

Abstract: A consideration intent system can include a computing device configured to receive an indication of an event occurring from a user device, obtain a set of parameters associated with the event and retrieve a set of item intent values corresponding to the set of items. The computing device is configured to determine a first value based on at least one parameter of the set of parameters and classify the event as one of: (i) low consideration intent and (ii) high consideration intent by inputting the set of item intent values and the first value as features to a machine learning algorithm. The computing device is configured to, based on the classification, identify a set of recommendation models, generate a set of recommended item identifiers by implementing at least one recommendation model of the set of recommendation models, and transmit the set of recommended item identifiers to the user device.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: Systems and methods for generating top-k recommendation using latent space representations generated by deep joint variational autoencoder processes are disclosed. A user identifier is received and a set of prior interactions associated with the user identifier is obtained. A set of latent space representations of the set of prior interactions is generated using a trained inference model. The trained inference model includes a joint variational autoencoder model. A set of k-recommended items is generated based on a comparison of the set of latent space representations of the set of prior interactions and a set of latent space representations of one or more items. A user interface including the set of k-recommended items is generated.

Abstract: A session-specific conversion determination system can include a computing device configured to receive real-time signals of an event occurring from a user device. The real-time signals include interaction parameters. The computing device is also configured to obtain a set of historical data based on the interaction parameters and a set of facets and generate a probability affinity for each facet of the set of facets by implementing a machine learning model using the interaction parameters and the set of historical data as features. The computing device is also configured to adjust a display of a set of recommended items based on the probability affinity for each facet of the set of facets and transmit the display of the set of recommended items to a user interface of the user device.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query. Other embodiments are disclosed herein.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of conversion scores, based on the set of features. In some examples, each conversion score of the plurality of conversion scores are associated with a particular user of the set of users and characterize a likelihood of a conversion event of the corresponding user changing from a trial-member status to a full-member status prior to a predetermined future time.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.