Abstract: A digital magazine server generates a model to associate topics with content items. To generate the model, the digital magazine server selects a set of content items that have been included in one or more digital magazines. For each content item of the set, the digital magazine server determines a distribution of concepts associated with a content item of the set based on characteristics of digital magazines including the content item of the set and a distribution of topics associated with the content item of the set based on words included in the content item. Additionally, the digital magazine server determines a parameter defining a relationship between the distributions of concepts and the distributions of topics associated with content items of the set. A model based on the distributions of concepts and of topics as well as the parameter is generated and stored for application to content items.

Abstract: A digital magazine server generates a model to associate topics with content items. To generate the model, the digital magazine server selects a set of content items that have been included in one or more digital magazines. For each content item of the set, the digital magazine server determines a distribution of concepts associated with a content item of the set based on characteristics of digital magazines including the content item of the set and a distribution of topics associated with the content item of the set based on words included in the content item. Additionally, the digital magazine server determines a parameter defining a relationship between the distributions of concepts and the distributions of topics associated with content items of the set. A model based on the distributions of concepts and of topics as well as the parameter is generated and stored for application to content items.

Abstract: A digital magazine server maintains information describing interactions with content items presented to users of the digital magazine server via digital magazines. Additionally, the digital magazine server maintains a model that associates topics with content items based on characteristics of digital magazines including the content items and characteristics of the content items. To improve recommendation of content items to users, the digital magazine server combines a model recommending content items based on maintained user interactions with the model associating topics with content items based on characteristics of the content items and of digital magazines including the content items. For example, the combined model generates a value for a content item that decreases a value obtained from prior user interactions with the content item by a reduction term based on one or more topics associated with the content item.

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.

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.