Abstract: Content items are presented to users based on sensitivity scores indicating sensitivity levels of users to relevance of content items to queries. A system receives a query from a target user, retrieves a set of search results responsive to the query, and retrieves a set of content items, each of which has a relevance score to the query. The system applies a machine learning model to user data of the target user to output a sensitivity score, indicating a sensitivity level of the target user to relevance of content item to the query. The system then selects one or more content items based on the sensitivity score and the relevance scores of the content items, incorporates the selected content items into the search results, and sends the search results with the selected content items for display to the target user.

Abstract: An online system trains a churn prediction model to attribute a churn event to one or more causal events. The churn prediction model receives customer features and online system features as inputs. Various causal events that occur affect one or more online system features. To avoid biasing the churn prediction model using input features that are related to possible causal events, the online system determines customer features and online system features based on customer interactions occurring in different time intervals. The customer features are determined from interactions in a time interval that is earlier than a time interval from which interactions are used to determine online system features. Such time segmenting decorrelates the features input to the model from the events, reducing potential bias from the causal events on the churn prediction model.

Abstract: An online system generates session-based recommendations for a user accessing an application of the online system. The online system receives, from one or more client devices, a sequence of actions performed by a user during a session of an application of an online system. The online system generates a sequence of tokens from the sequence of actions by tokenizing an action to a token representing a respective item identifier. The online system applies a transformer-based machine-learned model to the sequence of tokens to generate predictions for a set of items. The online system selects a subset of items based on the generated predictions for the set of items. The online system generates one or more recommendations to the user from the selected subset of items and displays the recommendations to the user.

Abstract: An online concierge system maintains information about items offered for purchase and users of the online concierge system. Based on prior purchases of items by users, the online concierge system trains a model to determine a likelihood of a user purchasing an item based on an embedding for the object and embedding for the user. The online concierge system identifies a collection of items and generates an embedding for the collection. The collection may be a cluster of items determined from similarities between embeddings of items. Alternatively, the collection may be a group of items having a common category. The online concierge system includes one or more collections of items along with individual items when recommending items for the users, so the trained model is applied to embeddings of the individual items and to embeddings of the one or more collections to generate recommendations for a user.

Abstract: An online concierge system accesses an order history database, which describes historical orders for a plurality of users. Each historical order comprises a list of items selected by a user from a plurality of items. The online concierge system determines user streaks, each user streak based on a number of consecutive orders placed by a user that include a particular item. The online concierge system calculates an overall streak distribution based on the user streaks. For each item of at least a subset of the plurality of items, the online concierge system calculates a streak statistic based on a corresponding subset of user streaks and the overall streak distribution. The online concierge system selects a set of staples items for a user based on the streak statistics for the set of staple items and generates a display for the user with a selectable list of the set of staple items.

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed

Abstract: In an approach, a method comprises using a server computer in a media content delivery system that is configured to selectively deliver a particular media title from among a library of titles, for a source title, generating title data that specifies an order of a plurality of titles that are related to the source title based on a plurality of stored probability values; wherein each probability value in the plurality of probability values represents, for each particular title of the plurality of titles, a likelihood of selecting the particular title after playing the source title; using the server computer, receiving title impression data, wherein the title impression data specifies a plurality of browsed titles that were browsed from among the plurality of titles but may have not been selected for interaction; using the server computer, receiving title interaction data, wherein the title interaction data specifies a plurality of selected titles that were selected for interaction from the plurality of browsed