Disclosed herein is a platform utilizing data processing to accelerate sales by personalizing user interactions based on user behavior. Network-based sales platforms allow merchants to offer goods or services to customers online. However, existing technical solutions may lack the functionality that allows determining what goods and services would be of interest to a particular customer. The lack of such functionality may result in low conversion rates. As a result of the poor conversion rates, merchants incur unnecessary expenses in blanket advertising to customers who are unlikely to buy the advertised goods and services.

Disclosed herein is a platform utilizing data processing to accelerate sales by personalizing user interactions based on user behavior. Network-based sales platforms allow merchants to offer goods or services to customers online. However, existing technical solutions may lack the functionality that allows determining what goods and services would be of interest to a particular customer. The lack of such functionality may result in low conversion rates. As a result of the poor conversion rates, merchants incur unnecessary expenses in blanket advertising to customers who are unlikely to buy the advertised goods and services.

A system performs factor selection processing, which includes performing a factor selection operation for selecting one or two or more factors from one or a plurality of factors; factor filtering processing which includes: determining, about each of the one or the plurality of factors before the factor selection processing is performed, whether a factor value for prediction input to a prediction model is within a range of a plurality of factor values for specimen used to identify the prediction model; and excluding a factor, a result of the determination of which is false, and outputting a factor not excluded; and prediction processing which includes calculating the prediction value of the prediction target by inputting, to the prediction model, a factor value for prediction about each of one or more factors including the factor selected in the factor selection processing and not including the factor excluded in the factor filtering processing.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize collaborative filtering and a reinforcement learning model having an actor-critic framework to provide digital content items across client devices. In particular, in one or more embodiments, the disclosed systems monitor interactions of a client device with one or more digital content items to generate item embeddings (e.g., utilizing a collaborative filtering model). The disclosed systems further utilize a reinforcement learning model to generate a recommendation (e.g., determine one or more additional digital content items to provide to the client device) based on the user interactions. In some implementations, the disclosed systems utilize the reinforcement learning model to analyze every negative and positive interaction observed when generating the recommendation. Further, the disclosed systems utilize the reinforcement learning model to analyze item embeddings, which encode the relationships among the digital content items, when generating the recommendation.

An information processing apparatus includes at least one processor, in which the processor is configured to acquire an image of a participant participating in an event, and output a two-dimensional code in which attribute information indicating an attribute of the participant estimated based on the image and identification information assigned for each participant are coded.

An information processing apparatus includes at least one processor, in which the processor is configured to acquire an image of a participant participating in an event, and output a two-dimensional code in which attribute information indicating an attribute of the participant estimated based on the image and identification information assigned for each participant are coded.

Embodiments described herein are related to systems and methods for forecasting demands for a transportation service. In one aspect, a set of neural network models may be implemented, where each neural network model can be configured to predict a booking status of a category of carriers on a corresponding date from a range of dates before a departure date. In one aspect, for each neural network model, a corresponding set of configuration values can be determined. Examples of the corresponding set of configuration values includes at least one of a number of layers, a number of neurons, and an activation function of the each neural network model. The set of neural network models can be constructed, according to corresponding sets of configuration values, and the constructed neural network models can be trained.

Embodiments described herein are related to systems and methods for forecasting demands for a transportation service. In one aspect, a set of neural network models may be implemented, where each neural network model can be configured to predict a booking status of a category of carriers on a corresponding date from a range of dates before a departure date. In one aspect, for each neural network model, a corresponding set of configuration values can be determined. Examples of the corresponding set of configuration values includes at least one of a number of layers, a number of neurons, and an activation function of the each neural network model. The set of neural network models can be constructed, according to corresponding sets of configuration values, and the constructed neural network models can be trained.

Embodiments described herein are related to systems and methods for offering an upgrade for a transportation service. In one aspect, a computer can receive a reservation request for a transportation service including an itinerary indicating a first location, a second location, and a first service category of the transportation service. The computer can determine a plurality of reposition estimates for a plurality of service categories including the first service category and one or more potential service categories, each reposition estimate being determined based upon the first location, the second location, and a particular service category. The computer can identify, as a second service category, the potential service category having the reposition estimate lower than the reposition estimate of the first service category in the reservation request. The computer can transmit to the client device an upgrade notification indicating an eligibility to update the itinerary to include the second service category.

Embodiments described herein are related to systems and methods for offering an upgrade for a transportation service. In one aspect, a computer can receive a reservation request for a transportation service including an itinerary indicating a first location, a second location, and a first service category of the transportation service. The computer can determine a plurality of reposition estimates for a plurality of service categories including the first service category and one or more potential service categories, each reposition estimate being determined based upon the first location, the second location, and a particular service category. The computer can identify, as a second service category, the potential service category having the reposition estimate lower than the reposition estimate of the first service category in the reservation request. The computer can transmit to the client device an upgrade notification indicating an eligibility to update the itinerary to include the second service category.