Abstract: An online concierge system maintains various attributes for each item. To optimize information about items displayed in an interface, the online concierge system selects a subset of attributes of an item for display based on an item category including the item. The online concierge system applies an attribute selection model to combinations of an item category and attributes associated with the item category. The attribute selection model selects one or more attributes for an item category using one or more of an engagement model trained from prior interactions by customers and an output of a large language model prompted to select relevant attributes based on the item category. When generating an interface including an item, the online concierge system includes the subset of attributes selected for an item category including the item in conjunction with the item.

Abstract: An online system may obtain an image of a receipt from a user, wherein the receipt includes a list of item identifiers and associated charges. The online system may provide a prompt to a first machine learning model including the image or extracted information from the image, and a request to provide descriptors of a list of items corresponding to the item identifiers in the image. The online system may receive from the first machine learning model as a response, the list of items and associated charges. The online system maps the list of items to one or more items in an item catalog of an online system. The online system may add the one or more items to an order associated with the user.

Abstract: Embodiments of the disclosure provide an image processing method and apparatus, an electronic device and a storage medium. The method includes: when it is detected that a main body to be processed on a display interface is a target main body, adding an augmented reality (AR) special effect model for the target main body; according to a current video frame and a previous historical video frame, determining a target display location of the AR special effect model in the current video frame; displaying the AR special effect model at the target display position to obtain a target special effect video frame corresponding to the current video frame.

Abstract: Embodiments of the present disclosure provide a method and apparatus for determining effect video, an electronic device, and a storage medium. The method includes: in response to detecting that a target effect template is triggered, retrieving a target configuration file; determining the target edit item corresponding to the target effect template and the edit parameter of the target edit item by parsing the target configuration file, and displaying the target edit item; in response to receiving an effect material to be edited corresponding to a target edit item, displaying an edit parameter, so as to determine a target effect material corresponding to the effect material to be edited based on an adjustment operation on the edit parameter; and based on the target effect material and a video stream to be processed, generating a target effect video with the video stream to be processed as background information.

Abstract: An online shopping concierge platform receives data indicating one or more customer interactions associated with a particular item offered by the online shopping concierge platform; identifies a plurality of different and distinct images of the particular item; generates, based at least in part on multiple different and distinct machine learning (ML) models and for each image of the plurality of different and distinct images, a composite score for the image; selects, based at least in part on its respective composite score, an image of the particular item to be presented to the customer; generates data describing a graphical user interface (GUI) comprising a listing of the particular item including the selected image; and communicates to a computing device associated with the customer the data describing the GUI such that the computing device associated with the customer renders and displays the listing.

Abstract: An online system presents a sponsored content page to a user in conjunction with a model serving system. The online system accesses a content page for a food item and identifies one or more sponsorship opportunities at the content page. The online system identifies one or more candidate sponsors for each sponsorship opportunity. The online system selects a bidding sponsor for the sponsorship opportunity from the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item. The online system provides a content page, a description of the sponsored item, and a request to generate a sponsored content page for the sponsorship opportunity to a model serving system. The online system receives a sponsored content page generated by a machine-learning language model at the model serving system and presents the sponsored content page to a user.

Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

Abstract: An online system obtains a target food from an order for a user and alcohol preferences from an order purchase history. The online system generates a prompt for a machine learning model to request alcohol candidates based on the target food category. The prompt includes the alcohol preferences, and requests for each alcohol candidate, a pairing score indicating how well the target food category pairs with the alcohol candidate and a user preference score indicating how well the alcohol candidate aligns with the alcohol preferences. The online system receives as output the candidate alcohol items. Each alcohol candidate has the pairing score, the user preference score, and a textual reason for scores. The online system matches at least one alcohol item from a catalog with each alcohol candidate. A subset of alcohol items is presented to the user as a carousel.

Abstract: Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.

Abstract: An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.

Abstract: An online system performs a task in conjunction with the model serving system or the interface system. The system generates a first prompt for input to a machine-learned language model, which specifies contextual information and a first request to generate a theme. The system provides the first prompt to a model serving system for execution by the machine-learned language model, receives a first response, and generates a second prompt. The second prompt specifies the theme and a second request to generate a third prompt for input to an image generation model that includes a third request to generate one or more images of one or more items associated with the theme. The system receives the third prompt by executing the model on the second prompt, provides the third prompt to the image generation model, and receives one or more images for presentation.

Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

Abstract: An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.

Abstract: This application relates to apparatus and methods for providing recommended items based on a search request. In some examples, a computing device determines a plurality of items for recommendation based on an item type in the search request from a user. The computing device then determines a model variant from a plurality of variants for each item based on a requested variant in the search request. The computing device generates item recommendations by modifying an item title for each of the plurality of items to indicate the corresponding model variant. The computing device then presents the item recommendations for the user's perusal.

Abstract: A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

Abstract: The present disclosure relates to methods and compounds for liquid phase oligonucleotide synthesis employing the use of small molecules with lipophilic groups. Methods for making an oligonucleotide by liquid phase oligonucleotide synthesis using the compounds described herein are also provided.

Abstract: The disclosure discloses a method and an apparatus for stylizing a three-dimensional model, an electronic device, and a storage medium. The method for stylizing the three-dimensional model includes: acquiring a to-be-stylized three-dimensional model and a stylized target image; and rendering the three-dimensional model by using a predetermined network to acquire a two-dimensional rendered image and spatial feature parameters of a pixel, and stylizing a texture feature in the two-dimensional rendered image based on the spatial feature parameters and the stylized target image to acquire a stylized three-dimensional model.

Abstract: Embodiments of the present disclosure provide an ablation catheter, comprising: a catheter, a handle and a connector which are connected in sequence, wherein a plurality of electrodes are provided in sequence at a distal end of the catheter, each of the electrodes has a corresponding electrode lead, which passes through the catheter and the handle and is connected to the connector; the catheter comprises an insulated inner catheter, the electrodes are provided on the insulated inner catheter, an insulated outer catheter is provided between the plurality of electrodes, electrode leads of the plurality of electrodes are respectively provided between the insulated inner catheter and the insulated outer catheter.

Abstract: The present disclosure relates to methods and compounds for liquid phase oligonucleotide synthesis employing the use of small molecules with lipophilic groups. Methods for making an oligonucleotide by liquid phase oligonucleotide synthesis using the compounds described herein are also provided.