Abstract: An online system receives an indication that a user is starting an order. The online system retrieves candidate contents for the user and provides prompts to a model serving system. The model serving system is configured to provide scores for the contents based on relevancy, a likelihood of user interaction, and a likelihood of the user purchasing an item associated with the content. The online system provides scores from the model serving system to a predicted click-through rate (pCTR) model. Based on the pCTR model scores, the online system ranks the candidate contents. The online system provides content for display to the user based on the ranked candidate contents.

Abstract: A superabrasive compact and a method of making the superabrasive compact are disclosed. A superabrasive compact may comprise a diamond body and a metallic substrate. The diamond body comprises diamond particles. Diamond particles may have a plurality of layers of inorganic hard coatings on surface of diamond particles. The plurality of layers of coatings may have thickness ranging from about 0.1% to about 20% of the size of the diamond particle. The metallic substrate may be in direct contact with the diamond body.

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.

Abstract: System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.

Abstract: The present disclosure discloses a continuous stirring transmission mechanism for tempering furnace workpiece transmission, including a heat treatment furnace body and a furnace body support, the heat treatment furnace body is fixed to the furnace body support, the heat treatment furnace body includes a furnace body shell, a heat preservation and insulation system and a heating system, the heating system supplies heat to the heat treatment furnace body; the mechanism further includes a driving mechanism and continuous stirring rotating rod assemblies. The continuous stirring transmission mechanism is provide for tempering furnace workpiece transmission; the problems of non-uniform heating of metal workpieces in the tempering process can be solved, and a heat utilization rate is improved.

Abstract: An online system publishes sponsored content items to users. To enable a publishing user to evaluate performance of a campaign including sponsored content items and identify modifications to improve the campaign, the online system trains a large language model (LLM). Information about previous campaigns and their performance, previously asked questions about the campaigns, and actions for modifying the campaigns are used to train the LLM. For a particular ad campaign, the online system generates a prompt for the LLM to generate a list of suggestions and corresponding actions. The online system generates an interface including the suggestions in conjunction with interface elements causing performance of one or more of the actions when selected by the publishing user.

Abstract: The present disclosure discloses a continuous stirring transmission mechanism for tempering furnace workpiece transmission, including a heat treatment furnace body and a furnace body support, the heat treatment furnace body is fixed to the furnace body support, the heat treatment furnace body includes a furnace body shell, a heat preservation and insulation system and a heating system, the heating system supplies heat to the heat treatment furnace body; the mechanism further includes a driving mechanism and continuous stirring rotating rod assemblies. The continuous stirring transmission mechanism is provide for tempering furnace workpiece transmission; the problems of non-uniform heating of metal workpieces in the tempering process can be solved, and a heat utilization rate is improved.

Abstract: Template-based tuning is performed on a generative machine learning model where a shared template is used to tune the generative machine learning model across multiple natural language tasks. When a natural language request to perform a natural language task is received, portions of a shared template to complete are identified as part of generating a prompt. The generative machine learning model is instructed according to the generated prompt and a response to the request is returned based on a result of the generative machine learning model.

Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

Abstract: An online system manages presentation of content items in various presentation contexts such as when the users are browsing pages or when the users have entered a search query. The online system trains a single unified machine learning model that predicts one or more likelihoods of a target event associated with presentation of a content item in the different presentation contexts. The learned model is applied to a set of candidate content items associated with a presentation opportunity in a specific context. Features that are inapplicable to the specific context may be masked when applying the model. The online system may select between the candidate content items based on the predicted likelihoods using the model trained across the multiple different contexts, such that the prediction for one context may be based in part on learned outcomes in other related contexts.

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Abstract: An online system receives an indication that a user is starting an order. The online system retrieves candidate contents for the user and provides prompts to a model serving system. The model serving system is configured to provide scores for the contents based on relevancy, a likelihood of user interaction, and a likelihood of the user purchasing an item associated with the content. The online system provides scores from the model serving system to a predicted click-through rate (pCTR) model. Based on the pCTR model scores, the online system ranks the candidate contents. The online system provides content for display to the user based on the ranked candidate contents.

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.