Abstract: A test execution and maintenance system may include an application learner configured to develop knowledge of operations of an application based on one or more learning test. The test execution and maintenance system then may include a test execution and repair manager configured to execute a functional test of the application, detect a failure of the functional test, and repair the functional test based on the knowledge of operations of the application. Responsive to detecting the failure of the functional test, the application learner may optionally perform a re-learning process and update the previously acquired knowledge of operations of the application.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine serer may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine serer may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine server may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine server may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine serer may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A digital magazine server generates a personalized digital magazine tailored for the interests of a user with subtopic specificity. The digital magazine server identifies topics of interest for a user and one or more subtopics related to each topic. For each subtopic, the digital magazine server infers whether the subtopic is of interest to the user based on prior user actions performed by the user or social signals describing the user's actions with content items, such as following content items from the user's influencers. For subtopics that are of interest, relevant content items are identified and boosted in comparison to other content items that are only relevant to the topic of interest. Thus, the digital magazine server generates a personalized content feed for the user with the boosted content items, thereby enabling more relevant presentation of content items that are likely to be of interest to the user.

Abstract: An online system automatically selects one or more content items in a digital magazine for recommendation based on a common theme of the content items and similarities of the content items. In one aspect, content items are associated with different latent topics. A latent topic identifies a theme or a concept of related content items, where the theme is determined based on a probability of words appearing together in one or more content items sharing the identified theme. From a set of content items on a common latent topic with a subject content item, one or more content items may be automatically identified based on content proximity scores of the set of content items with respect to the subject content item. One or more content items having a content proximity score within a predetermined range with the subject content item are selected for recommendation to a user.