Abstract: To select the content to be presented to the user, a first latent vector is determined for a content item based on a first object associated with the content item. A second latent vector is determined for the content item based on a second object associated with the content item. A content item vector is then determined based on the first and second latent vectors. Furthermore, a user vector is determined based on interactions of the user with the first set of content objects and the second set of content objects. A score indicative of the likelihood of the user interacting with the content item is determined based on the content item vector and the user vector.

Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.

Abstract: An online system tracks stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisements performed by online system users. When the online system identifies an opportunity to present an advertisement to a viewing user, the online system identifies content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. A score is computed for various advertisements based at least in part on correlations between content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. The online system selects candidate advertisements to evaluate for presentation to the viewing user based on the scores.

Abstract: An online system maintains topic vectors associated with various content items, where a vector associated with a content item indicates a topic vector of a content item. Words in a content item and context traits describing presentation of the words in the content item are used by the online system to determine a topic vector associated with the content item. When a subject content item for display, the online system determines the topic vector associated with the subject content item and identifies topic vectors associated with other content items nearest to the topic vector associated with the subject content item in a vector space through application of one or more clustering algorithms to the topic vectors. Content items associated with the identified topic vectors are indicated as similar to the subject content item by the online system.

Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.

Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.

Abstract: Fingerprint-based content identification is described. Bin-counting detects linear trends based on data points that are generated from a set of query fingerprints, which are extracted from a query content item and a matching set of corresponding reference content items that are extracted from a reference content item. RANSAC may detect trends based on confidence scores that are computed for multiple candidate content items. The trends detected for the candidate content items are compared against a threshold in order of decreasing confidence score until a trend that satisfies the threshold is found. A ranking-based mechanism for fingerprint-based content identification may use a bin-counting or RANSAC-based trend detection mechanism to identify duplicate or multiple reference content items that all match the same query content item.

Abstract: Fingerprint-based content identification is described. Bin-counting detects linear trends based on data points that are generated from a set of query fingerprints, which are extracted from a query content item and a matching set of corresponding reference content items that are extracted from a reference content item. RANSAC may detect trends based on confidence scores that are computed for multiple candidate content items. The trends detected for the candidate content items are compared against a threshold in order of decreasing confidence score until a trend that satisfies the threshold is found. A ranking-based mechanism for fingerprint-based content identification may use a bin-counting or RANSAC-based trend detection mechanism to identify duplicate or multiple reference content items that all match the same query content item.

Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.