Abstract: Example implementations relate to interface generation including interface elements representative of a co-located resource when a user device is located within a predetermined geofenced area and a corresponding user has historical interactions with the co-located resource. In an example, a request for an interface and location data are received from a user device. The location data corresponds to a location of the user device when the request for the interface was generated. In response to determining the location data is within a predetermined geofenced area, a resource use probability of a resource for the user device is generated using a resource affinity model based at least in part on historical usage of the resource. In response to determining the resource use probability is above a predetermined threshold, instructions are transmitted to the user device that modify the interface to include an interface element representative of a resource usage.

Abstract: Systems and methods for providing item recommendations to increase retention rates of customers are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, an anchor item to be displayed to the customer via a user interface; determining relevance scores between the anchor item and a plurality of candidate items; determining retention scores between the anchor item and the plurality of candidate items; generating, using at least one machine learning model, a ranked list of recommended items based on the plurality of candidate items, the relevance scores and the retention scores; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the anchor item on the user interface.

Abstract: A semiconductor structure includes a first transistor device disposed on a substrate, the first transistor device having a first orientation, and a second transistor device disposed on the first transistor device in a stacked configuration, the second transistor device having a second orientation different than the first orientation.

Abstract: Examples provide a system and method for automatically tagging items and forming item-level user cohorts using a generative artificial intelligence (GenAI) model. A GenAI model generates item-level persona labels for a subset of untagged items sampled from a category of items. An item-level persona reflects user behaviors and/or preferences associated with specific items and types of items. The labeled item data, including the persona identification (ID) label, is used to train a deep neural net (DNN) labeling model to label untagged items with persona IDs for item-level personas. Customized item recommendations for each tagged item is mapped to a user cohort and used to generate item recommendations customized at the item-level. The DNN labeling model is periodically tested, evaluated, and retrained using sample tagged item data from the GenAI model to reduce the DNN model error rate and improve accuracy of the DNN model persona predictions.

Abstract: Systems and methods for attribute recommendation are disclosed. Transaction data related a user is received and attribute recommendations for the user are generated based on the transaction data. The attribute recommendations are generated by a variational inference model configured using a transaction matrix and a loyalty matrix. A set of N recommendations is generated by ranking the generated attribute recommendations based on a combined transaction score and loyalty score and a user interface is generated including the set of N recommendations.

Abstract: In some examples, a system may be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data. Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified in catalogue data, clique data. Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data. Further, the system may, for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: A method for provisioning automatic configurations may involve detecting a connection between a computing device and an additional computing device that runs a first protocol and is communicatively coupled to a controller via a network fabric. In one example, the method may also involve determining, via a second protocol, that the computing device is unconfigured for operation in the network fabric. Additionally or alternatively, the method may further involve enabling the controller to configure the computing device for operation in the network fabric via a provisioning mechanism in response to determining that the computing device is unconfigured for operation in the network fabric. Various other devices, systems, and methods are also disclosed.

Abstract: A system and method for analyzing the quality of data in a server or network component a log file. The system imports a quality log analyzer (QLA) plugin into an integrated developer environment (IDE). The developer application code is compiled within the IDE. The QLA plugin invokes a log analyzer suggestion module (LASM) during the developer application code runtime. During the application code runtime, the LASM, reads the developer application code to parse server log strings associated with the developer application code. The LASM analyzes the developer application code server log strings for similarities, and calculates the similarity between two or more application code server log strings to determine if they are identical, similar, or unrelated. The system uses these comparisons along with an optimized server log rules engine to determine which application code server log strings can be optimized to reduce use of the server log memory.

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: Systems and methods of generating personalized interfaces are disclosed. An interface generation request is received and, in response to receiving the interface generation request, a plurality of user-relevant element bundles is selected. The plurality of user-relevant bundles is selected in part by a weighted random sampling process. In response to selecting the plurality of user-relevant element bundles, a set of interface elements associated with each of the plurality of user-relevant element bundles is ranked based on interface element relevance to a user associated with the interface generation request. Instructions to cause generation of an interface including at least one of the plurality of user-relevant element bundles and at least a top-ranked interface element selected from the set of interface elements associated with the at least one of the plurality of user-relevant element bundles are transmitted to a user device.

Abstract: An example method for handling a secure virtual event in a virtual environment by an electronic device includes receiving at least one input from a first user avatar to create at least one virtual event within the virtual environment. The method further includes identifying at least one second user avatar for participating the at least one created virtual event based on the received input from the first user avatar and a nature of the virtual event and determining at least one security policy corresponding to at least one action associated with the at least one second user avatar and at least one parameter within the at least one created virtual event.

Abstract: System and methods for dynamic distributed model loading are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical data associated with previously loaded models, receiving a model loading request associated with a first model via a user interface, identifying one or more model parameters associated with the first model, generating a score value associated with the first model based on the one or more model parameters, based on the score value, partitioning the first model into a plurality of first model segments, ranking the plurality of first model segments with a plurality of second model segments associated with a second model, and executing each of the plurality of first model segments and the plurality of second model segments based on the ranking.

Abstract: A system 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, perform certain acts. The acts can include obtaining training data. The acts also can include training candidate recommendation models and an adversarial exposure model using the training data. The acts additionally can include generating recommendations based on a selected recommendation model of the candidate recommendation models. Other embodiments are described.

Abstract: Systems and methods for post transaction seasonal item recommendations are disclosed. In some embodiments, a current seasonal time window associated with a seasonal event and some seasonal product types is determined. Based on historical transaction data of the seasonal product types, a first seasonal index score is computed for each item, and a second seasonal index score is computed for each product type including one or more items. A seasonal rank score is generated for each item based on the first seasonal index score and the second seasonal index score, such that the items in the historical transaction data are ranked based on their respective seasonal rank scores. Based on the ranked items and a transaction order from a user, a list of recommended items is generated and displayed to the user.

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 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: A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Abstract: Systems and methods for predicting and motivating customers to provide reviews are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a request for potential reviewers; selecting, based on the request, a plurality of customers who have purchased items but have not provided any review for the purchased items; computing, using at least one machine learning model, review probabilities based on feature data related to the plurality of customers and transactions involving the purchased items, wherein each review probability is a probability of a corresponding customer providing a review; generating, from the plurality of customers, a ranked list of customers based on the review probabilities and reviewer segmentation data; and transmitting to the computing device the ranked list of customers to be reminded for providing reviews.

Abstract: Systems and methods for providing item recommendations to increase retention rates of customers are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, an anchor item to be displayed to the customer via a user interface; determining relevance scores between the anchor item and a plurality of candidate items; determining retention scores between the anchor item and the plurality of candidate items; generating, using at least one machine learning model, a ranked list of recommended items based on the plurality of candidate items, the relevance scores and the retention scores; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the anchor item on the user interface.