Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).

Abstract: An online system determines an estimated conversion rate for sponsored content items placed on content publishers and on the online system. The estimated conversion rate can be determined by a machine learning model trained using data describing content campaigns, content publishers, and online system users. This data is collected by the online system from content publishers and/or content campaigns that report conversion rates to the online system. By determining a ratio of estimated conversion rates with third party content on the content publisher against those on the online system, the online system can determine a publisher quality score for that content publisher. The online system uses the publisher quality score to normalize third party value contributions toward placing sponsored content on content publishers and the online system. Thus, disparities in the intrinsic value across publishers are diminished as third party value contributions are normalized based on the publisher conversion rates.

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).

Abstract: An online system maintains information identify a context in which sponsored content items were presented to users. A context in which a sponsored content item was presented to a user identifies additional content presented to the user prior to the sponsored content item, and may identify additional content presented in conjunction with the sponsored content item. The online system identifies users to whom at least one sponsored content item was presented in a context and generates characteristics for the context based on characteristics of users who were presented with at least one sponsored content item in the context. When the online system receives a request to present sponsored content items in the context that does not identify an online system user, the online system selects sponsored content items for the request based on the generated characteristics for the context.

Abstract: An online system maintains information identify a context in which sponsored content items were presented to users. A context in which a sponsored content item was presented to a user identifies additional content presented to the user prior to the sponsored content item, and may identify additional content presented in conjunction with the sponsored content item. The online system identifies users to whom at least one sponsored content item was presented in a context and generates characteristics for the context based on characteristics of users who were presented with at least one sponsored content item in the context. When the online system receives a request to present sponsored content items in the context that does not identify an online system user, the online system selects sponsored content items for the request based on the generated characteristics for the context.

Abstract: Systems, methods, and computer-readable storage media are provided for utilizing part-of-speech (POS) tagging of both the words included in a search query and the words included in potential search result documents to improve query alteration accuracy and search result ranking. Upon receiving a search query, POS tags are assigned to the words comprising the query to create query word-tag pairs. The query word-tag pairs are utilized to reformulate the query and are compared with document word-tag pairs included in a plurality of potential search result documents to determine a degree of similarity. The degree of similarity is utilized as an input in scoring and/or ranking the relevance of the potential search result documents with respect to one another.

Abstract: Systems, methods, and computer-readable storage media are provided for utilizing part-of-speech (POS) tagging of both the words included in a search query and the words included in potential search result documents to improve query alteration accuracy and search result ranking. Upon receiving a search query, POS tags are assigned to the words comprising the query to create query word-tag pairs. The query word-tag pairs are utilized to reformulate the query and are compared with document word-tag pairs included in a plurality of potential search result documents to determine a degree of similarity. The degree of similarity is utilized as an input in scoring and/or ranking the relevance of the potential search result documents with respect to one another.

Abstract: Improved search result relevance is provided for name segment searches performed by a general web search engine. Entity-related information is mined from web documents and search engine query logs, and metadata is indexed in a search system index. The metadata may include information identifying entity homepages, entity web pages at high quality top sites, other entity-related web pages, entity equivalent data, and/or entity misspellings data. The indexed metadata is employed to provide improved search results relevance for search queries that include an entity's name by improving the ranking of search results corresponding with entity-relevant web pages.