Abstract: One or more systems and/or methods for providing conversion prospecting for dynamic content recommendation are provided. A model is trained, using positive events and negative events, to predict conversion-given-click probabilities of users and products. The model is utilized to generate a prediction of a conversion-given-click probability that a user will perform an action in relation to a product. The model is used to generate a bid for the user and the product based upon the conversion-given-click probability, a content provider bid for the product, and a target cost per action. The bid is used to determine whether the product is to compete in an auction hosted by a content serving platform that selects and transmits content items of products to devices for display to users.

Abstract: One or more systems and/or methods for providing trending prospecting for dynamic content recommendation are provided. A model is trained to predict eligibility scores of users to content items based upon popularity of the content items and similarities of the users with other users that have engaged with the content items. As part of training, positive and negative events are input into the model. For each user and content item pair, the model generates an eligibility score corresponding to a ratio between a number of users that are similar to a user and have engaged with the content item to a number of users that are similar to the user and are part of a user population represented by the positive events and the negative events. The eligibility scores are used to generate and train the model. The model is used to select and provide content items to computing devices.

Abstract: One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.

Abstract: One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.

Abstract: One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.

Abstract: One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.