Abstract: The disclosure herein describes context-based candidate data generation with self-refinement. An example disclosed operation includes: generating a batch of candidate data using a large language model (LLM) based generator, formatting the batch of candidate data according to a plurality of formatting constraints, evaluating, using a sequence of evaluators, a candidate of the batch of candidate data, determining whether any evaluator of the sequence of evaluators fails the candidate, based on determining that at least one evaluator of the sequence of evaluators fails the candidate, identifying a first failing evaluator, mapping an output of the first failing evaluator to a refiner instruction, refining, using a LLM based refiner, the candidate based on the refiner instruction, reformatting the refined candidate, and re-evaluating the refined candidate, until earlier of either a maximum of refine attempts is reached or all evaluators of the sequence of evaluators are successful.

Abstract: Example implementations relate to systems and methods for detecting disengagement. In an example, a system determines, by a risk identifier, predetermined risk labels based on the user data. The system determines, using a risk evaluator that receives input features, a risk of disengagement for a user. The system determines, using a risk interpreter that receives the input features and the predetermined risk labels, a disengagement reason for the user. The system also determines, based on the risk of disengagement and the disengagement reason, a disengagement prevention incentive for the user. The system further presents, at a computing device associated with the user, a user interface element for interacting with the disengagement prevention incentive.

Abstract: Some embodiments provide systems to provide metadata abstraction for a comparison interface. An example system includes a non-transitory machine-readable medium storing instructions that, when executed by a processing resource, may cause the resource to: compile textual metadata associated with an item, determine a ranked list of use cases for the item based in part on using one or more prompts to a language model, at least one of the one or more prompts includes the textual metadata, receive, via a client interface provided to a client device, a selection of the item and a comparison item; and select a highlighted use case to display with the item in a comparison interface of the client interface, wherein the highlighted use case is selected based on the ranked list associated with the item and a ranked list of use cases associated with the comparison item in the use case database.

Abstract: Example implementations relate to systems and methods for generating customized incentives to increase engagement. In an example, a system receive first data and second data distinct from the first data. The system determines, using a disengagement evaluator, disengagement scores for candidates in the first data, and selects a set of candidates from the first data having disengagement scores above a disengagement threshold to form disengagement candidates. The system determines, using an engagement evaluator, engagement scores for users that are based on the disengagement candidates and the second data, selects a set of the users having engagement scores above an engagement threshold to form engagement candidates. The system generates a notification for a user of the engagement candidates that includes an incentive for user interaction, and transmits the notification to a computing device the user of the engagement candidates.

Abstract: Example implementations relate to systems and methods for detecting disengagement. In an example, a system determines, by a risk identifier, predetermined risk labels based on the user data. The system determines, using a risk evaluator that receives input features, a risk of disengagement for a user. The system determines, using a risk interpreter that receives the input features and the predetermined risk labels, a disengagement reason for the user. The system also determines, based on the risk of disengagement and the disengagement reason, a disengagement prevention incentive for the user. The system further presents, at a computing device associated with the user, a user interface element for interacting with the disengagement prevention incentive.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital customer insights based on historical customer data. In some examples, a computing device obtains user data for a user. In response, the computing device receives a plurality of product types relevant to the user and their corresponding relevance scores. For each product type, the computing device then receives a set of attributes, where each attribute is associated with an affinity score for the user. The computing device determines, for each product type, an overall score for each attribute and product type pair based on the relevance score for the product type and the affinity score for the corresponding attribute. At least one attribute and product type pair is presented to the user based on the corresponding overall score.

Abstract: Some embodiments provide systems to control digital communications comprising: a transceiver; a processing resource; and a medium storing instructions executed to cause the processing resource to: during a current session, determine acquisition execution intent prediction features; trigger a query to a first trained model; identify a first inferred acquisition execution type based on the acquisition execution intent prediction features; repeatedly evaluate according to a predefined interval whether to trigger a refresh of the first inferred acquisition execution type; trigger a first refresh query; identify, using the first trained model, a second inferred acquisition execution type; identify a set of one or more items corresponding to the in-session search; filter the set to a sub-set of items that satisfy the search and comply with the second inferred acquisition execution type; and control the data communications transceiver to transmit response data in controlling the remote client computing device to ren

Abstract: In some embodiments, apparatuses and methods are provided herein useful to provide data generation and abstraction for items of a platform. Some embodiments, system may include a database storing data associated with a plurality of items, a processing resource, and a machine readable medium storing instructions that when executed cause the processing resource to: generate, using a first trained model, a first prompt to query at least one LLM to design a series of steps to determine one or more insights associated with a target item; receive the series of steps from the at least one LLM; generate, using an additional trained model, a step specific prompt to query the at least one LLM to provide a step specific insight factor; receive the step specific insight factor; and output data to cause a display of a user device to display the target item and the insight.

Abstract: Example implementations relate to communication element selection in a network environment. In an example, a plurality of user features is received and input data derived from the plurality of user features is processed using a window logic and provided to an attention-based machine learning model. A plurality of communication elements is provided to the attention-based machine learning model, which assigns one or more weights to each of the plurality of communication elements for a subsequent time period based on the plurality of user features obtained from a preceding time period. A score for each of the plurality of communication elements based on the one or more weights for each of the plurality of communication elements is calculated and an interface including a communication element of the plurality of communication elements having a highest calculated score is generated.

Abstract: Examples may be related to cross-category recommendation. An example may involve identifying an anchor item to be presented to a user via a user interface, wherein the anchor item is in a first category; and evaluating, using a machine learning model, a degree of cross-category intent of the user based at least partially on real-time interaction data of the user. The degree of cross-category intent may indicate a likelihood that the user will engage with any item in a second category that is different from the first category. An eligibility of the user to receive an item recommendation in the second category can be determined based on the degree of cross-category intent. A recommended item in the second category may be determined based on the eligibility, and presented to the user together with the anchor item in the user interface.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to generate personalized content. In some embodiments, a system comprising a processing resource; a machine readable medium storing instructions that, when executed, cause the processing resource to: aggregate, session data during a client session; update, periodically and during the interaction session, one or more inference indicators associated with the client in an inference cache storage, the one or more inference indicators being determined via a trained machine learning model using the session data and historical data in a historical data database; identify a trigger event based on client interactions; retrieve, in response to the trigger event, at least one inference indicator associated with the client from the inference cache storage; and generate a personalized content for display on the client device based on the at least one inference indicator retrieved from the inference cache storage.

Abstract: Examples related to predicting user behaviors are disclosed. An example may involve: obtaining current behavior data of a user within a current user session; obtaining historical behavior data of the user during a past time period; determining, using at least one natural language model, context data that is relevant for predicting future behavior data of the user, wherein the context data is determined based on the current behavior data and the historical behavior data; generating, using a prediction model, a ranked list of elements related to the future behavior data of the user based on the context data; and transmitting the ranked list of elements to a computing device associated with the current user session.

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.