Abstract: Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user using the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.

Abstract: Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score and a number of times the entity has been selected for ranking. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user based on the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains one or more metrics associated with user interaction with a set of content items in a member segment of a social network. Next, the system ranks the set of content items by the one or more metrics. The system then generates a content pool for the member segment based on the ranking of the content items. Finally, the system creates a content feed from the content pool for presentation to a user in the member segment.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains one or more metrics associated with user interaction with a set of content items in a member segment of a social network. Next, the system ranks the set of content items by the one or more metrics. The system then generates a content pool for the member segment based on the ranking of the content items. Finally, the system creates a content feed from the content pool for presentation to a user in the member segment.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of content pools for a user, wherein each content pool in the set of content pools includes a set of content items associated with user activity in a member segment of a social network. Next, the system calculates a set of probabilities of clicking the content items in the content pool. The system then uses the probabilities to order the content items with other content items from other content pools into a content feed for the user. Finally, the system presents the content feed to the user.

Abstract: Disclosed are apparatus and methods for facilitating contextual selection of advertisements for displaying online via a computer network. In general, user features in the form of text are provided in conjunction with web page content for contextual advertisement matching. In one embodiment, a request for an advertisement to be displayed in a current web page that has been requested by a current user is received. The current user is associated with one or more current user characteristics from a plurality of different user characteristics, and the current web page has an associated content. A mapping model and the one or more current user characteristics are used to obtain a plurality of user-relevant terms for each of the one or more current user characteristics. A combination of the content of the current web page and obtained user-relevant terms are provided for selecting an advertisement for displaying with the current web page based on such combination.

Abstract: An online advertising selects online advertisements for display on a network location taking into account a probability that a candidate online advertisement will receive a click on a particular website. The system may determine a network location identity of the network location and transform a set of advertisements into a set of ranked advertisements. The system may determine an advertisement rank of a first advertisement among the set of ranked advertisements. The system then may generate a click probability value. The click probability value may reflect a click probability of the first advertisement by dividing an exponent of a weighted sum of the network location identity and the advertisement rank by one plus the exponent of the weighted sum of the network location identity and the advertisement rank.

Abstract: Disclosed are apparatus and methods for apparatus and methods for facilitating contextual selection of advertisements for displaying online via a computer network. In general, user features in the form of text are provided in conjunction with web page content for contextual advertisement matching. In one embodiment, a request for an advertisement to be displayed in a current web page that has been requested by a current user is received. The current user is associated with one or more current user characteristics from a plurality of different user characteristics, and the current web page has an associated content. A mapping model and the one or more current user characteristics are used to obtain a plurality of user-relevant terms for each of the one or more current user characteristics. A combination of the content of the current web page and obtained user-relevant terms are provided for selecting an advertisement for displaying with the current web page based on such combination.

Abstract: An online advertising selects online advertisements for display on a network location taking into account a probability that a candidate online advertisement will receive a click on a particular website. The system may determine a network location identity of the network location and transform a set of advertisements into a set of ranked advertisements. The system may determine an advertisement rank of a first advertisement among the set of ranked advertisements. The system then may generate a click probability value. The click probability value may reflect a click probability of the first advertisement by dividing an exponent of a weighted sum of the network location identity and the advertisement rank by one plus the exponent of the weighted sum of the network location identity and the advertisement rank.