An item information providing method according to example embodiments may include checking a plurality of products corresponding to a search term received from a user who uses the service, and providing a first page that includes a first region in which a list of the plurality of products is displayed and a second region in which a filtering interface configured to filter the list of the plurality of products displayed in the first region according to a user's input is displayed.

Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.

In an embodiment, the disclosure provides computer-implemented systems and methods for providing graph-based recommendations of digital media collaborators for content creators. In an embodiment, the disclosure provides computers programmed to implement a networked, online platform for facilitating collaboration between content creators. In an example embodiment, the platform provides a system for recommending a collaborator for a particular content creator to create content with, of a specific content type. In another example embodiment, the platform provides a system for recommending a collaborator for a particular content creator to create content with, without restricting the content type, using a community detection algorithm. In embodiments, recommendations may be made partly based on centrality measures of creator nodes on a network graph programmatically calculated between content nodes of that network graph, or content nodes of a community detected in the network graph. Recommendations may also be informed by characterizations of followers of content creators.

Embodiments of the present disclosure relate to a method, a device, and a computer program product for user behavior prediction. In some embodiments, at a client, a first user behavior embedding engine in the client generates behavior prediction information of a target user based on feature information of the target user. The client sends the behavior prediction information of the target user to a server, and receives information about a target item recommended for the target user from the server. Such method enables user privacy-related information to be processed only locally, thereby not only ensuring user privacy and security, but also significantly reducing overall resource overhead.

An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system generates a recipe vector for each recipe based on items included in a recipe. A dimension of a recipe vector identifies an item included in a corresponding recipe and may include an importance score of the item to the recipe. The importance score of an item to a recipe is based on a term frequency of the item in the recipe and an inverse document frequency of the item across multiple recipes. The online concierge system determines overlap between items in recipe vectors an order vector generated from items included in an order from a user and selects a recipe for the user based on overlapping items in the recipe vector and in the order vector.

A method can include obtaining personal data of a user and generating, based at least in part on the personal data of the user, a taste profile of the user. The taste profile can include a set of food characteristics that corresponds to one or more food preferences of the user. The method can include obtaining contextual data that corresponds to a location of the user. The method can include generating, based at least in part on the taste profile and the contextual data, a food recommendation. The food recommendation can include a predicted food preference of the user. The method can include transmitting the food recommendation to the user.

Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.

Methods, apparatus, and processor-readable storage media for determining transaction-related user intentions using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to digital behavior of a user during a transaction-related session on one or more electronic commerce websites; classifying the user into one of multiple categories by processing the obtained data pertaining to the digital behavior of the user using artificial intelligence techniques, wherein the multiple categories correspond to multiple predicted levels of user intention to complete a transaction; determining, based on the classification of the user and the obtained data pertaining to the digital behavior of the user, at least one reason why the user may not complete a transaction during the transaction-related session; and performing one or more automated actions based at least in part on the at least one determined reason.

A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model-specific results to generate an ensemble recommendation result; and transmitting the ensemble result from the content recommender to said querying customer content server.

The present disclosure relates to a method for providing a user with a contextualized evaluation of a fit of frames of eyeglasses to their face. In particular, the present disclosure relates to a method, comprising receiving user data describing features of the face of the user, receiving equipment data describing features of the eyeglass frame, generating, according to a first model, values for a set of specific criteria describing compatibility between the face of the user and the eyeglass frame, the first model trained to associate user data and equipment data with values of specific criteria, generating, according to a second model, a value of a global criterion based on the generated values for the set of specific criteria, the second model trained to associate the values of specific criteria with values of global criteria, determining a message characterizing the eyeglass frame with respect to the face of the user.