Abstract: An online system uses a model to determine affinities of users for geographical locations. Using the affinities, which may indicate travel-related preferences of the users, the online system may customize content items to include content captured by client devices of other users of the online system. For example, the online system presents to a particular user a content item including a photo or video of a geographical location captured by a camera of a client device of another user who is connected to the particular user. The model may implement, for example, collaborative filtering or other machine learning techniques to determine commonalities between users' travel affinities. Additionally, the model may determine latent properties or temporal trends of user preferences based on training data including historical actions performed on the online system or social data. The model may also classify different types of geographical locations.

Abstract: An online system provides navigation information customized using travel preferences of users. The online system receives actions performed by users that may indicate their geographical locations of interest. The online system may use a model to predict a user's level of interest in destination geographical locations. The online system generates navigation information or travel information that describes routes from origin geographical locations of users to destination geographical locations to which the users are likely to travel. The online system transmits the navigation information to client devices for presentation as personalized or dynamically-created content items to users. The online system may generate navigation information using catalogs describing routes between geographical locations. For instance, the catalog indicates a vehicle for navigation along a route, as well as origin and destination geographical locations.

Abstract: An online system provides navigation information customized using travel preferences of users. The online system receives actions performed by users that may indicate their geographical locations of interest. The online system may use a model to predict a user's level of interest in destination geographical locations. The online system generates navigation information or travel information that describes routes from origin geographical locations of users to destination geographical locations to which the users are likely to travel. The online system transmits the navigation information to client devices for presentation as personalized or dynamically-created content items to users. The online system may generate navigation information using catalogs describing routes between geographical locations. For instance, the catalog indicates a vehicle for navigation along a route, as well as origin and destination geographical locations.

Abstract: An online system provides navigation information customized using travel preferences of users. The online system receives actions performed by users that may indicate their geographical locations of interest. The online system may use a model to predict a user's level of interest in destination geographical locations. The online system generates navigation information or travel information that describes routes from origin geographical locations of users to destination geographical locations to which the users are likely to travel. The online system transmits the navigation information to client devices for presentation as personalized or dynamically-created content items to users. The online system may generate navigation information using catalogs describing routes between geographical locations. For instance, the catalog indicates a vehicle for navigation along a route, as well as origin and destination geographical locations.

Abstract: An online system uses a model to determine affinities of users for geographical locations. Using the affinities, which may indicate travel-related preferences of the users, the online system may customize content items to include content captured by client devices of other users of the online system. For example, the online system presents to a particular user a content item including a photo or video of a geographical location captured by a camera of a client device of another user who is connected to the particular user. The model may implement, for example, collaborative filtering or other machine learning techniques to determine commonalities between users' travel affinities. Additionally, the model may determine latent properties or temporal trends of user preferences based on training data including historical actions performed on the online system or social data. The model may also classify different types of geographical locations.

Abstract: An online system customizes content items by processing a matrix of temporal values using different types of filters. A temporal filter filters the matrix using time-based parameters such as start dates and end dates of the temporal values. An action filter filters the matrix using actions performed by the users and may also generate a likelihood that a user will interact with a content item including a selected temporal value. A category filter filters the matrix using user profile information of the users received by the online system. Users may be more likely to interact with content items selected by the online system using the filtering process because the content items include temporal values or other information that are customized to preferences of particular users. For example, the customized content items include information describing lodgings that the users are likely to select.

Abstract: An online system uses rules and/or machine learning models to provide travel related content items to users. The online system may determine when a user is likely to travel and provide the content items in advance of a trip. The online system may also provide content items during a trip that indicate modifications to the user's itinerary, for example, adding a rental car, upgrading a flight ticket, or upgrading a hotel room. Further, the online system may provide a content item after a user has checked out of a hotel that describes a loyalty program of the hotel. In one example, the online system trains machine learning models using feature vectors derived based on trips taken by a population of users of the online system and itinerary information from third parties. The content items may be generated based on information provided from the third parties.

Abstract: A content item maintained by an online system has a creative presenting content including a product from an application associated with a third party system. When the online system identifies an opportunity to present the content item to the user, the online system selects a product associated with the third party system based on content from one or more third party systems accessed by the user. The online system includes the content item with the creative including content from the application including the selected product in one or more selection processes. If the content item with the creative including content from the application including the selected product is presented to the user and the online system receives an indication the application was installed on a client device, the online system provides instructions to the client device to present content associated with the selected product.