The present disclosure provides a method [200] and system [100] for providing a relevant product via a digital media platform. The method comprises receiving, a user query of a user. The method thereafter comprises determining, a personalization score corresponding to the user query based at least on an affinity of the user to digital content/s and influencer/s present on social media platform/s. The method thereafter encompasses determining, a diversification score corresponding the digital content/s. Further the method comprises determining, a relevance score of standard relevance parameter/s corresponding to the user query. The method thereafter encompasses determining, a ranking score corresponding to the user query based at least on the personalization score, the diversification score and the relevance score. The method thereafter encompasses providing via the digital media platform, the relevant product in response to the user query based on the ranking score corresponding to the user query.

A content provider can provide content, such as advertisements or other promotional material, to a recipient. A distribution system of the content provider can receive a query and select one or more advertisements and a user interface for presenting the advertisements based on the query, information associated with the user, a set of queries received from the user, and/or other information. A model for selecting the user interface can be generated by serving available user interfaces randomly or pseudo-randomly and monitoring user interaction with the served user interfaces. The model can be updated during regular use based on the performance of the user interfaces.

To simplify retrieval of items from a database that at least partially satisfy a received query, an online concierge system trains a model that outputs scores for items from the database without initially retrieving items for evaluation by the model. The online concierge system pre-trains the model using natural language inputs corresponding to items from the database, with a natural language input including masked words that the model is trained to predict. Subsequently, the model is refined using multi-task training where a task is trained to predict scores for items from the received query. The online concierge system selects items for display in response to the received query based on the predicted scores.

An interface is rendered on a user-operated device. Selections are made with respect to a first set of options by the user operating the interface. A list of potential food dishes for further selection by the user is presented within the interface based on the selections. A food dish selection is received from the user through the interface. A recipe for the selected food dish is generated and presented within the interface based on the food dish selection and a list of ingredients needed for the recipe are generated as a shopping list for the user to obtain at a store. In an embodiment, a navigation route to obtain the ingredients within the store is provided to the user when the user is at the store, allowing the user to traverse the route within the store and obtain each of the ingredients.

A merchandise specification system has: a keystone correction processing unit that performs a keystone correction process on photographed image information obtained by photographing the display shelf to generate front-facing image information; and a merchandise identification information specification processing unit that specifies merchandise on a face in the front-facing image information.

Systems and methods for calculating and displaying implicit popularity of products are disclosed. In embodiments, a computer-implemented method, comprises: determining, by a computing device, a set of consumer actions related to a product based on recorded consumer interaction data, wherein the set of consumer actions comprise a plurality of different physical actions of multiple consumers over a span of time; aggregating, by the computing device, the set of consumer actions by type of physical action over time to produce aggregated action event data; calculating, by the computing device, an implicit popularity number for the product based on the aggregated action event data; and sending, by the computing device, a display category to a product display device associated with the product based on the implicit popularity number, wherein the display category represents a measure of consumer popularity of the product.

A customer who visits an online marketplace to view information regarding an item formed from fabric may request and receive instructions for producing a sample of the fabric that mimics the sensations of touch and feel of the fabric. The sample may be produced using an automated fabricator, such as a 3D printer, associated with a client device from which the customer made the request. The instructions may include or describe a digital model of the fabric, customized for the automated fabricator. In particular, the sample may be formed from various plastics or other materials that are accessible to the automated fabricator, and need not be formed from the actual materials that are included in the fabric.

Techniques are described for automatically extracting items from a user's browse history on one or more e-commerce websites, and automatically searching other e-commerce websites and sources for offers (including coupons, deals, and/or promotions) for those items and/or related items and/or stores. Such automated techniques allow users to gain the benefit of online offers from various sources, without having to go through the effort of manually searching for such offers. Instead, the system and method automatically locate such offers based on the user's browse history, without requiring any action to be taken on the part of the user.

An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.

A computer system for enhanced personal property replacement may be provided. The computer system may include at least one processor in communication with at least one memory device. The at least one processor may programmed to receive a plurality of user preferences transmitted from a user computer device associated with a user, receive a request from the user to cover a claim, determine a first plurality of items for the user based upon the claim and the plurality of user preferences, and/or transmit, to a retailer computer device associated with a retailer, an order for the determined first plurality of items with the retailer to be delivered to the user at an address associated with the user.