Abstract: An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

Abstract: An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

Abstract: An online system displays an interface to users including slots in which sources from a list of sources of items (e.g., physical items, content items) are presented. The user may select a source via the interface to view items provided by, or associated with, the source. To simplify a user identifying a desired source, the online system includes sources that a user is likely to select as well as new sources in the list. To balance the competing interests of relevance of sources with which the user previously interacted and discovery of new sources, the online system selects an allocation of slots for new sources and for sources with prior interaction based on interactions by users in the geographic regions with different allocations of slots. Based on the selected allocation of slots, the online system selects specific retailers for each slot using ranking models corresponding to different slots.

Abstract: An online system generates text-based tags for item sources to dynamically generate customized clusters of the item sources for a user. The online system selects a set of item categories within the taxonomy based on interaction rate data of users with the item source. The online system uses these selected categories to generate tags for the item source. The online system generates a prompt for an LLM to generate an item source cluster for a set of item sources. The prompt includes the generated tags for the item sources and instructions on how to select item sources to include in the cluster based on the tags. The online system receives a response from the LLM that specifies which item sources to include in the item source cluster and the online system transmits instructions to a client device to present the item source cluster in a user interface.

Abstract: A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.

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 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 uses an offline iterative clustering process to evaluate the performance of a set of content selection frameworks. To perform an iteration of the iterative clustering process, an online system clusters the testing example data into a set of clusters. An online system computes a set of framework scores for each of the generated clusters. An online system computes an improvement score for each cluster based on the performance scores of the clusters. To determine whether to perform another iteration, an online system computes an aggregated improvement score based on the improvement scores of the clusters. If an online system determines that the aggregated improvement score does not meet the threshold, an online system performs another iteration of the process above. When an online system finishes the iterative process, an online system outputs the improvement scores of the most-recent iteration.

Abstract: A system accesses user data describing characteristics of a user and generates a content item score for each content item of a plurality of content items. The system generates the content item score by applying a machine-learning model to the user data, and then generates a plurality of content bundles. The system also generates a bundle score for each content bundle based on corresponding content item scores for the content item associated with each content bundle, randomly selects a bundle of the plurality of content bundles based on the generated bundle scores, and transmits the randomly selected bundle to a client device associated with the user for display to the user. Finally, the system applies the model to each of the generated training examples and updates the parameters of the model based on the model output.

Abstract: An online concierge system uses a new treatment engine to score users for applying treatments of a new treatment type. The new treatment engine uses treatment models to generate treatment lift scores for the user. The new treatment engine applies an aggregation function model to the treatment lift scores to generate an aggregated lift score for the user. If the aggregated lift score exceeds a threshold, the new treatment engine applies a treatment of the new treatment type to the user. The new treatment engine trains the aggregation function model based on training examples used to train the treatment models. For a training example associated with a particular treatment type, the new treatment engine uses a target lift score generated by the treatment model for the treatment type to evaluate the performance of the aggregation function model, and to update the aggregation function model accordingly.

Abstract: An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

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.