Abstract: Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system generates a carousel display structure definition of the relevancy-ranked content items. The system transmits the carousel display structure definition of the relevancy-ranked content items and the content items to the client device.

Abstract: Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.

Abstract: Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system generates a carousel display structure definition of the relevancy-ranked content items. The system transmits the carousel display structure definition of the relevancy-ranked content items and the content items to the client device.

Abstract: Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.

Abstract: Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.

Abstract: Methods and systems provide for rich media presentation of recommendations in generative media. In one embodiment, the system presents, via a trained generative AI, a set of media content to a user in a communication session within a platform, the media content including a number of sorted recommended items; monitors and quantifies one or more user responses from the user to the presented media content and one or more associated generative responses from the trained generative AI; based on the monitoring and quantifying, detects one or more mentions of the user to one of the plurality of sorted recommended items; generates, from the one or more detected mentions, one or more labeled training examples; and further trains the trained generative AI based on the one or more labeled training examples to improve the presentation of the media content in future communication sessions.

Abstract: Methods and systems provide for cross-relevant refinement of generative artificial intelligence models for creative content across multiple platforms. In one embodiment, the system receives creative content related to a first product listing for a product within a first platform, user engagement data for a user of a second platform, and one or more pieces of contextual information; trains a refinement of a second generative AI model for dynamic creative content generation for a modified version of the first product listing for the second platform; generates and displays one or more pieces of creative content for a second product listing to be published on the second platform; receives feedback regarding user engagement with the pieces of creative content in terms of whether an engagement objective has been achieved; and refines the first generative AI model and the second generative AI model via a network of cross-refinement.

Abstract: Methods and systems provide for dynamic contextual generation of creative content for product listings. In one embodiment, the system receives initial product facts for a product, user engagement data for a user of a platform, and one or more pieces of contextual information related to how the product will be viewed within the platform; uses this data to train a generative AI model for dynamic creative content generation for the listing; uses the trained generative AI model to dynamically generate creative content for the listing; displays the creative content for the listing on a client device associated with the user; receives feedback regarding user engagement with the creative content in terms of whether an engagement objective has been achieved; and refines the generative AI model based on the received feedback, including optimizing the generative AI model to generate or modify the creative content to achieve the engagement objective.

Abstract: Methods and systems provide for dynamically optimized recommendations in generative media. In one embodiment, the system receives, through a conversational interface, input submissions from a user engaging in a conversation with a generative artificial intelligence (AI) system; generates, via the generative AI system, a search query for a search engine backend of the platform; sends the search query to the search engine backend of the platform to retrieve at least a subset of a prompt as input to the generative AI system, the subset of the prompt including a sorted list of search results from the search engine backend; processes the prompt to generate a set of personalized recommendations for the user; and presents, within the platform presented at the client device, the set of personalized recommendations for the user, the presentation incorporating media content representing at least a portion of the search result items.

Abstract: Methods and systems provide for a unified presentation of cross-platform content to a user visiting a platform. In one embodiment, the system connects a client device associated with a user to a first content platform; receives a request from the client device to present content to the user at the first content platform; receives content associated with one or more additional content platforms; determines a subset of the content to present to the user; standardizes the subset of the content in a format to be used at the first content platform; presents the subset of the content to the user at the first content platform; processes a set of unified cross-platform metrics for user events related to the user interacting with the subset of the content at the first content platform; and provides a report of the set of unified cross-platform metrics for the user events.

Abstract: An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.

Abstract: Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.

Abstract: An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third-party system, and receives prediction improvement data from the third-party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third-party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.

Abstract: An online system accesses a list of features used as input into a predictor to predict a performance metric for content presented to users. The online system computes importance scores for one or more of the features. A ranked list of categories is created, with each category having one or more sub-categories. For each feature having a computed importance score, the online system assigns, for each attribute in the ranked list of attributes for that feature, the feature to a sub-category in one of the categories in the ranked list of categories that has the same rank as the attribute in the ranked list of attributes for the feature, where the sub-category is associated with a label that corresponds with the attribute. For each sub-category in each category, a cumulative score is computed for the sub-category based on the importance scores of the features of that sub-category.

Abstract: An online system ranks content eligible for presentation to an online system user based on a prediction made by a general model or a specific model indicating a likelihood that the user will interact with a content item, in which the specific model has a higher latency than the general model. The online system determines which prediction to use for the ranking by balancing the benefit of a more accurate prediction made by the specific model against the higher latency of the specific model. The online system outputs the predicted likelihood from one of the models based on the determination, ranks content items eligible for presentation to the user based on the output, and selects content item(s) for presentation to the user based on the ranking. The online system may log the predicted likelihoods from both models, the outputted predicted likelihood, and information describing the performance of the content item.

Abstract: A system receives a first plurality of impressions associated with a first set of features. Labels for the first plurality is generated based on the first set of features. A machine learning model is trained based on the first set of features and the labels. A second plurality of impressions associated with a second set of features is received. A first estimated probability measuring conversion likelihood when impression delivery occurs is generated based on applying the second plurality and the second set of features to the model. A plurality of holdout impressions associated with a third set of features is identified. A second estimated probability measuring conversion likelihood when impression delivery is withheld is generated based on applying the plurality of holdout impressions and the third set of features to the model. Increased valued (e.g., lift) is estimated based on subtracting the second estimated probability from the first estimated probability.

Abstract: Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.

Abstract: Methods and systems provide for a unified presentation of cross-platform content to a user visiting a platform. In one embodiment, the system connects a client device associated with a user to a first content platform; receives a request from the client device to present content to the user at the first content platform; receives content associated with one or more additional content platforms; determines a subset of the content to present to the user; standardizes the subset of the content in a format to be used at the first content platform; presents the subset of the content to the user at the first content platform; processes a set of unified cross-platform metrics for user events related to the user interacting with the subset of the content at the first content platform; and provides a report of the set of unified cross-platform metrics for the user events.

Abstract: Disclosed herein is a system and method to determine whether to place an advertisement to a user requesting an address from the user. The system can iteratively determine multiple advertisement metrics of multiple advertisements to obtain multiple metrics. An advertisement metric among the multiple advertising metrics can indicate the value of placing the advertisement to the user. The system can rank multiple advertisements based on the multiple advertisement metrics and present a predetermined percentage of top-ranking advertisements among the multiple advertisements.

Abstract: An online system accesses a model attribute store, which stores configuration information and model performance scores for a plurality of models, each model used to predict performance metrics regarding content from a third party system presented to users of the online system. The online system trains a meta-model classifier using the models in the model attribute store, the meta-model classifier trained to predict, for a candidate model, a predicted model performance score of that candidate model. The online system also generates a plurality of candidate models for input to the meta-model classifier, each of the plurality of candidate models including a distinct set of configuration information. The predicted model performance scores for a selected candidate model in the plurality of candidate models is computed using the meta-model classifier, and the online system transmits a report to the third party system indicating predicted model performance score for the selected candidate model.